Augmented intelligence explainability with recourse

ABSTRACT

A method, system and computer-readable storage medium for performing a cognitive information processing operation. The cognitive information processing operation includes: receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via an augmented intelligence system, the augmented intelligence system executing on a hardware processor of an information processing system, the augmented intelligence system and the information processing system providing a cognitive computing function; performing an explainability with recourse operation, the explainability with recourse operation providing an assurance explanation regarding the cognitive computing function; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates in general to the field of computers andsimilar technologies, and in particular to software utilized in thisfield. Still more particularly, it relates to a method, system andcomputer-usable medium for providing augmented intelligence system (AIS)assurance.

Description of the Related Art

In general, “big data” refers to a collection of datasets so large andcomplex that they become difficult to process using typical databasemanagement tools and traditional data processing approaches. Thesedatasets can originate from a wide variety of sources, includingcomputer systems, mobile devices, credit card transactions, televisionbroadcasts, and medical equipment, as well as infrastructures associatedwith cities, sensor-equipped buildings and factories, and transportationsystems. Challenges commonly associated with big data, which may be acombination of structured, unstructured, and semi-structured data,include its capture, curation, storage, search, sharing, analysis andvisualization. In combination, these challenges make it difficult toefficiently process large quantities of data within tolerable timeintervals.

Nonetheless, big data analytics hold the promise of extracting insightsby uncovering difficult-to-discover patterns and connections, as well asproviding assistance in making complex decisions by analyzing differentand potentially conflicting options. As such, individuals andorganizations alike can be provided new opportunities to innovate,compete, and capture value.

One aspect of big data is “dark data,” which generally refers to datathat is either not collected, neglected, or underutilized. Examples ofdata that is not currently being collected includes location data priorto the emergence of companies such as Foursquare or social data prior tothe advent of companies such as Facebook. An example of data that isbeing collected, but may be difficult to access at the right time andplace, includes the side effects of certain spider bites while anaffected individual is on a camping trip. As another example, data thatis collected and available, but has not yet been productized or fullyutilized, may include disease insights from population-wide healthcarerecords and social media feeds. As a result, a case can be made thatdark data may in fact be of higher value than big data in general,especially as it can likely provide actionable insights when it iscombined with readily-available data.

SUMMARY OF THE INVENTION

A method, system and computer-usable medium are disclosed for performingcognitive inference and learning operations.

In one embodiment, the invention relates to a method for performing acognitive information processing operation, the method comprising:receiving data from a plurality of data sources; processing the datafrom the plurality of data sources to provide cognitively processedinsights via an augmented intelligence system, the augmentedintelligence system executing on a hardware processor of an informationprocessing system, the augmented intelligence system and the informationprocessing system providing a cognitive computing function; performingan explainability with recourse operation, the explainability withrecourse operation providing an assurance explanation regarding thecognitive computing function; and, providing the cognitively processedinsights to a destination, the destination comprising a cognitiveapplication, the cognitive application enabling a user to interact withthe cognitive insights.

In another embodiment, the invention relates to a system comprising: ahardware processor; a data bus coupled to the hardware processor; and anon-transitory, computer-readable storage medium embodying computerprogram code, the non-transitory, computer-readable storage medium beingcoupled to the data bus, the computer program code interacting with aplurality of computer operations and comprising instructions executableby the hardware processor and configured for: receiving data from aplurality of data sources; processing the data from the plurality ofdata sources to provide cognitively processed insights via an augmentedintelligence system, the augmented intelligence system executing on ahardware processor of an information processing system, the augmentedintelligence system and the information processing system providing acognitive computing function; performing an explainability with recourseoperation, the explainability with recourse operation providing anassurance explanation regarding the cognitive computing function; and,providing the cognitively processed insights to a destination, thedestination comprising a cognitive application, the cognitiveapplication enabling a user to interact with the cognitive insights.

In another embodiment, the invention relates to a non-transitory,computer-readable storage medium embodying computer program code, thecomputer program code comprising computer executable instructionsconfigured for: receiving data from a plurality of data sources;processing the data from the plurality of data sources to providecognitively processed insights via an augmented intelligence system, theaugmented intelligence system executing on a hardware processor of aninformation processing system, the augmented intelligence system and theinformation processing system providing a cognitive computing function;performing an explainability with recourse operation, the explainabilitywith recourse operation providing an assurance explanation regarding thecognitive computing function; and, providing the cognitively processedinsights to a destination, the destination comprising a cognitiveapplication, the cognitive application enabling a user to interact withthe cognitive insights.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention may be better understood, and its numerousobjects, features and advantages made apparent to those skilled in theart by referencing the accompanying drawings. The use of the samereference number throughout the several figures designates a like orsimilar element.

FIG. 1 depicts an exemplary client computer in which the presentinvention may be implemented;

FIG. 2 is a simplified block diagram of an augmented intelligence system(AIS);

FIG. 3 is a simplified block diagram of an AIS reference model;

FIG. 4 is a simplified block diagram of an AIS platform;

FIG. 5 shows a simplified block diagram of components associated with acognitive process foundation;

FIG. 6 is a simplified block diagram of a plurality of AIS platformsimplemented within a hybrid cloud environment;

FIG. 7 shows components of a plurality of AIS platforms implementedwithin a hosted/private/hybrid cloud environment;

FIGS. 8 a and 8 b are a simplified process diagram showing theperformance of cognitive process promotion operations;

FIG. 9 is a simplified process diagram showing phases of a cognitiveprocess lifecycle;

FIGS. 10 a through 10 f show operations performed in a cognitive processlifecycle;

FIGS. 11 a and 11 b are a simplified process flow showing the lifecycleof cognitive agents implemented to perform AIS operations;

FIG. 12 is a simplified block diagram of an AIS used to performpattern-based continuous learning operations;

FIG. 13 is a simplified block diagram of components associated with anAIS governance and control framework implemented to provide AISassurance;

FIG. 14 shows a chart of input data points used to generatecounterfactuals;

FIGS. 15 a through 15 f show a simplified depiction of the generation ofcounterfactuals; and

FIGS. 16 a and 16 b show a generalized flowchart showing the performanceof AIS assurance operations.

DETAILED DESCRIPTION

A method, system and computer-usable medium are disclosed for providingaugmented intelligence system (AIS) assurance. Certain aspects of theinvention reflect an appreciation that augmented intelligence is nottechnically different from what is generally regarded as generalartificial intelligence (AI). However, certain aspects of the inventionlikewise reflect an appreciation that typical implementations ofaugmented intelligence are more oriented towards complementing, orreinforcing, the role human intelligence plays when discoveringrelationships and solving problems. Likewise, various aspects of theinvention reflect an appreciation that certain advances in general AIapproaches may provide different perspectives on how computers andsoftware can participate in tasks that have previously been thought ofbeing exclusive to humans.

Certain aspects of the invention reflect an appreciation that processesand applications employing AI models have become common in recent years.However, certain aspects of the invention likewise reflect anappreciation that known approaches to building, deploying, andmaintaining such processes, applications and models at significant scalecan be challenging. More particularly, various technical hurdles canprevent operational success in AI application development anddeployment. As an example, empowering development teams to more easilydevelop AI systems and manage their end-to-end lifecycle can provechallenging.

Accordingly, certain aspects of the invention may reflect anappreciation that the ability to orchestrate a pipeline of AI componentsnot only facilitates chained deployment of an AI system, but will likelyreduce implementation intervals while simultaneously optimizing the useof human and computing resources. Likewise, certain aspects of theinvention reflect an appreciation that achieving consistency across AIimplementations may be facilitated by easily sharing machine learning(ML) models within the context of a standardized application modelingand execution language. In particular, such an approach may beadvantageous when it is agnostic to common application developmentplatforms and database conventions.

Certain aspects of the invention likewise reflect an appreciation thatthe development of ML models is often a small, but important part of theAI development process. However, getting ML models developed by datascientists and then putting them into production requires time andresources, both of which may be limited. Likewise, certain aspects ofthe invention reflect that AI systems are generally complex.Accordingly, a repeatable approach that reduces the skill required todevelop and deploy AI systems can assist in achieving scalability of AIinitiatives.

Likewise, certain aspects of the invention reflect an appreciation thatAI, and the ML models they employ, has begun to play an increasinglyimportant role in our society. Certain aspects of the invention likewisereflect an appreciation that a number of questions arise from the use ofML models and the consequences of the decisions they make. For example,how did the model arrive at its outcome? If an individual receives anunfavorable outcome from the model, what is their recourse? Is theresomething in their personal lives that can be changed to result in adifferent outcome? Likewise, has the model been unfair to a particulargroup? How easily can the model be deceived?

Accordingly, certain aspects of the invention likewise reflect there areethical, moral, and social obligations for researchers, developers, andorganizations alike to ensure their ML models are designed, implemented,and maintained responsibly. Various aspects of the invention likewisereflect an appreciation that one approach to achieving responsibledesign, implementation, and maintenance of ML models is to provideauditability of their fairness, robustness, transparency, andinterpretability.

Certain aspects of the invention likewise reflect an appreciation thatknown approaches to providing such auditability can prove to becumbersome, time-consuming, and at times, error-prone, especially whenthe internal design particulars of an ML model are unknown. Furthermore,certain aspects of the invention likewise reflect an appreciation thatknown approaches to ML model auditability typically focus on issuesindividually, not in a unified manner. Moreover, such approacheslikewise lack the ability to audit an ML model acting as a black box,whose internal design particulars are unknown.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention. The computer readable storage medium can be atangible device that can retain and store instructions for use by aninstruction execution device. The computer readable storage medium maybe, for example, but is not limited to, an electronic storage device, amagnetic storage device, an optical storage device, an electromagneticstorage device, a semiconductor storage device, or any suitablecombination of the foregoing.

A non-exhaustive list of more specific examples of the computer readablestorage medium includes the following: a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), a staticrandom access memory (SRAM), a portable compact disc read-only memory(CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk,a mechanically encoded device such as punch-cards or raised structuresin a groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as SMALLTALK, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

FIG. 1 is a generalized illustration of an information processing system100 that can be used to implement the system and method of the presentinvention. The information processing system 100 includes a processor(e.g., central processor unit or “CPU”) 102, input/output (I/O) devices104, such as a display, a keyboard, a mouse, a touchpad or touchscreen,and associated controllers, a hard drive or disk storage 106, andvarious other subsystems 108. In certain embodiments, the informationprocessing system 100 may also include a network port 110 operable toconnect to a network 140, which is likewise accessible by a serviceprovider server 142. The information processing system 100 likewiseincludes system memory 112, which is interconnected to the foregoing viaone or more buses 114. System memory 112 further comprises operatingsystem (OS) 116 and in certain embodiments may also comprise anaugmented intelligence system (AIS) 118. In these and other embodiments,the AIS 118 may likewise comprise a cognitive agent composition platform120, a cognitive process orchestration platform 126, and an AISgovernance and assurance framework 128. In certain embodiments, thecognitive agent composition platform 120 may include a cognitive skillcomposition platform 122. In certain embodiments, the informationprocessing system 100 may be implemented to download the AIS 118 fromthe service provider server 142. In another embodiment, thefunctionality of the AIS 118 may be provided as a service from theservice provider server 142.

In certain embodiments, the AIS governance and assurance framework 128may be implemented to perform an AIS assurance operation, described ingreater detail herein. In certain embodiments, the AIS assuranceoperation may include the performance of an AIS impartiality assessmentoperation, an AIS robustness assessment operation, an AIS explainabilityoperation, an AIS explainability with recourse operation, or acombination thereof, as likewise described in greater detail herein. Incertain embodiments, the AIS assurance operation may be performed on aservice provider server 142. In certain embodiments, performance of theAIS assurance operation may be provided as an AIS assurance service. Incertain embodiments, the AIS assurance service may be referred to as AISTrust as a Service (TaaS).

In certain embodiments, the AIS 118 may be implemented to performvarious cognitive computing operations. As used herein, cognitivecomputing broadly refers to a class of computing involving self-learningsystems that use techniques such as spatial navigation, machine vision,and pattern recognition to increasingly mimic the way the human brainworks. To be more specific, earlier approaches to computing typicallysolved problems by executing a set of instructions codified withinsoftware. In contrast, cognitive computing approaches are data-driven,sense-interpretation, insight-extracting, problem-solving,recommendation-making systems that have more in common with thestructure of the human brain than with the architecture of contemporary,instruction-driven computers.

To further differentiate these distinctions, traditional computers mustfirst be programmed by humans to perform specific tasks, while cognitivecomputing systems learn from their interactions with data and humansalike, and in a sense, program themselves to perform new tasks. Tosummarize the difference between the two, traditional computers aredesigned to calculate rapidly. In contrast, cognitive computing systemsare designed to quickly draw inferences from data and gain newknowledge.

Cognitive computing systems achieve these abilities by combining variousaspects of artificial intelligence, natural language processing, dynamiclearning, and hypothesis generation to render vast quantities ofintelligible data to assist humans in making better decisions. As such,cognitive computing systems can be characterized as having the abilityto interact naturally with people to extend what either humans, ormachines, could do on their own. Furthermore, they are typically able toprocess natural language, multi-structured data, and experience much inthe same way as humans. Moreover, they are also typically able to learna knowledge domain based upon the best available data and get better,and more immersive, over time.

It will be appreciated that more data is currently being produced everyday than was recently produced by human beings from the beginning ofrecorded time. Deep within this ever-growing mass of data is a class ofdata known as “dark data,” which includes neglected information, ambientsignals, and insights that can assist organizations and individuals inaugmenting their intelligence and deliver actionable insights throughthe implementation of cognitive processes.

As used herein, a cognitive process broadly refers to an instantiationof one or more associated cognitive computing operations, described ingreater detail herein. In certain embodiments, a cognitive process maybe implemented as a cloud-based, big data interpretive process thatlearns from user engagement and data interactions. In certainembodiments, such cognitive processes may be implemented to extractpatterns and insights from dark data sources that are currently almostcompletely opaque. Examples of dark data include disease insights frompopulation-wide healthcare records and social media feeds, or from newsources of information, such as sensors monitoring pollution in delicatemarine environments.

In certain embodiments, a cognitive process may be implemented toinclude a cognitive application. As used herein, a cognitive applicationbroadly refers to a software application that incorporates one or morecognitive processes. In certain embodiments, a cognitive application maybe implemented to incorporate one or more cognitive processes with otherfunctionalities, as described in greater detail herein.

Over time, it is anticipated that cognitive processes and applicationswill fundamentally change the ways in which many organizations operateas they invert current issues associated with data volume and variety toenable a smart, interactive data supply chain. Ultimately, cognitiveprocesses and applications hold the promise of receiving a user queryand immediately providing a data-driven answer from a masked data supplychain in response. As they evolve, it is likewise anticipated thatcognitive processes and applications may enable a new class of “sixthsense” processes and applications that intelligently detect and learnfrom relevant data and events to offer insights, predictions and advicerather than wait for commands. Just as web and mobile applications havechanged the way people access data, cognitive processes and applicationsmay change the way people consume, and become empowered by,multi-structured data such as emails, social media feeds, doctors'notes, transaction records, and call logs.

However, the evolution of such cognitive processes and applications hasassociated challenges, such as how to detect events, ideas, images, andother content that may be of interest. For example, assuming that therole and preferences of a given user are known, how is the most relevantinformation discovered, prioritized, and summarized from large streamsof multi-structured data such as news feeds, blogs, social media,structured data, and various knowledge bases? To further the example,what can a healthcare executive be told about their competitor's marketshare? Other challenges include the creation of acontextually-appropriate visual summary of responses to questions orqueries.

FIG. 2 is a simplified block diagram of an augmented intelligence system(AIS) implemented in accordance with an embodiment of the invention. Asused herein, augmented intelligence broadly refers to an alternativeconceptualization of general artificial intelligence (AI) oriented tothe use of AI in an assistive role, with an emphasis on theimplementation of cognitive computing, described in greater detailherein, to enhance human intelligence rather than replace it. In certainembodiments, an AIS 118 may be implemented to include a cognitive agentcomposition platform 120 and a cognitive process orchestration platform126.

In certain embodiments, the cognitive agent composition platform 120 maybe implemented to compose cognitive agents 250, which are in turnorchestrated by the cognitive process orchestration platform 126 togenerate one or more cognitive insights 262, likewise described ingreater detail herein. As used herein, a cognitive agent 250 broadlyrefers to a computer program that performs a task with minimal guidancefrom users and learns from each interaction with data and human users.As used herein, as it relates to a cognitive agent 250 performing aparticular task, minimal guidance broadly refers to the provision ofnon-specific guidelines, parameters, objectives, constraints,procedures, or goals, or a combination thereof, for the task by a user.For example, a user may provide specific guidance to a cognitive agent250 by asking, “How much would I have to improve my body mass index(BMI) to lower my blood pressure by twenty percent?” Conversely, a usermay provide minimal guidance to the cognitive agent 250 by asking,“Given the information in my current health profile, what effect wouldimproving my BMI have on my overall health?

In certain embodiments, one or more cognitive agents 250 may beimplemented as deployable modules that aggregate the logic, data andmodels required to implement an augmented intelligence operation. Incertain embodiments, a particular cognitive agent 250 may be implementedto be triggered by other cognitive agents 250, timers, or by externalrequests. In certain embodiments, a cognitive agent 250 may be composedfrom other cognitive agents 250 to create new functionalities. Incertain embodiments, a cognitive agent 250 may be implemented to exposeits functionality through a web service, which can be used to integrateit into a cognitive process or application, described in greater detailherein. In certain embodiments, cognitive agents 250 may be implementedto ingest various data, such as public 202, proprietary 204,transaction, social 208, device 210, and ambient 212 data, to provide acognitive insight 262 or make a recommendation.

In certain embodiments, the cognitive agent composition platform 120 maybe implemented to use cognitive skills 226, input/output services,datasets, and data flows, or a combination thereof, to compose acognitive agent 250. In certain embodiments, a cognitive agent 250 maybe implemented with an integration layer. In certain embodiments, theintegration layer may be implemented to provide data to a particularcognitive agent 250 from a various data sources, services, such as a webservice, other cognitive agents 250, or a combination thereof. Incertain embodiments, the integration layer may be implemented to providea user interface (UI) to a cognitive agent 250. In certain embodiments,the UI may include a web interface, a mobile device interface, orstationary device interface.

In certain embodiments, the cognitive agent composition platform 120 maybe implemented to include a cognitive skill composition platform 122. Incertain embodiments, the cognitive skill composition platform 122 may beimplemented to compose a cognitive skill 226. As used herein, acognitive skill 226 broadly refers to the smallest distinct unit offunctionality in a cognitive agent 250 that can be invoked by one ormore inputs to produce one or more outputs.

In certain embodiments, a cognitive skill 226 may be implemented toexecute an atomic unit of work, which can be triggered by one or moreinputs to produce one or more outputs. In certain embodiments, theinputs and outputs may include services, managed content, databaseconnections, and so forth. In certain embodiments, cognitive skills 226may be implemented to be connected via input/output units, or synapses,which control the flow of data through an associated cognitive agent250.

In certain embodiments, one or more cognitive skills 226 may beimplemented to provide various disjointed functionalities in a cognitiveagent 250. In certain embodiments, such functionalities may includeingesting, enriching, and storing data from a data stream, training andtesting a machine learning (ML) algorithm to generate an ML model, andloading data from an external source, such as a file. In certainembodiments, such functionalities may likewise include transforming theraw data into a dataset for further processing, extracting features froma dataset, or invoking various services, such as web services familiarto those of skill in the art.

As used herein, a cognitive model 222 broadly refers to a machinelearning model that serves as a mathematical representation of areal-world process that can be facilitated by a cognitive computingoperation. In certain embodiments, the cognitive skill compositionplatform 122 may be implemented to compose a cognitive skill 226 fromone or more cognitive models 222. In certain embodiments, theimplementation of a cognitive model 222 may involve the implementationof two cognitive actions 224. In certain embodiments, the firstcognitive action 224 may be implemented to train the cognitive model 222and the second cognitive action 224 may be implemented to makepredictions based upon a set of unlabeled data to provide a cognitiveinsight 262.

In certain embodiments, a cognitive action 224 may be implemented as afunction, a batch job, or a daemon, all of which will be familiar toskilled practitioners of the art. In certain embodiments, cognitiveactions 224 may be implemented to be decoupled from a particularcognitive skill 226 such that they may be reused by other cognitiveskills 226. In various embodiments, a cognitive action 224 implementedas a batch job may be configured to run at certain intervals or betriggered to run when a certain event takes place.

In certain embodiments, a cognitive skill 226 may be implemented toinclude a definition identifying various dataset input requirements,cognitive insight 262 outputs, and datasets needed to complete thecognitive skill's 226 associated cognitive actions 224. In certainembodiments, an output of one cognitive skill 226 may be used as theinput to another cognitive skill 226 to build complex cognitive agents250. In various embodiments, certain cognitive skills 226 may beimplemented to control the flow of data through an associated cognitiveagent 250. In various embodiments, a cognitive skill 226 may beimplemented as a modular entity to interface a particular cognitiveagent 250 to certain external applications and Application ProgramInterfaces (APIs). In certain embodiments, a cognitive skill 226 may beimplemented to perform extract, transform, load (ETL) operations uponthe output of another cognitive skill 226, thereby serving as a wrapperfor an ML classifier or regressor.

As used herein, an input/output service broadly refers to a live linkthat is implemented to send and receive data. In certain embodiments,input/output services may be defined in input/output pairs that requireand deliver a payload to and from a cognitive agent 250. In certainembodiments, public 202, proprietary 204, transaction, social 208,device 210, and ambient 212 data may be ingested and processed by theAIS 118 to generate one or more datasets. As used herein, a datasetbroadly refers to a type of data input a cognitive agent 250 may beimplemented to ingest. In certain embodiments, such a dataset may beimplemented to include a definition that includes the source of the dataand its corresponding schema.

Various embodiments of the invention reflect an appreciation that theimplementation of cognitive skills 226 in certain embodiments maystreamline, or otherwise facilitate, the construction of certaincognitive agents 250. In various embodiments, certain cognitive skills226 may be implemented as micro services and published in a repositoryof AIS components, described in greater detail herein, as ready-to-useunits, which can be mixed and matched between cognitive computingprojects. Certain embodiments of the invention reflect an appreciationthat the ability to adopt an assembly model that supports the mixing andmatching of cognitive skills 226 between cognitive computing projectsmay minimize the effort required to rewrite code for new cognitiveagents 250, and by extension, shorten development cycles.

As shown in FIG. 2 , examples of cognitive skills 226 used by thecognitive agent composition platform 1202 to generate cognitive agents250 include semantic analysis 228, goal optimization 230, collaborativefiltering 232, and common sense reasoning 234. Other examples of suchcognitive skills 226 include natural language processing 236,summarization 238, temporal/spatial reasoning 240, and entity resolution242. As used herein, semantic analysis broadly refers to performingvarious analysis operations to achieve a semantic level of understandingabout language by relating syntactic structures.

In certain embodiments, various syntactic structures may be related fromthe levels of phrases, clauses, sentences, and paragraphs to the levelof the body of content as a whole, and to its language-independentmeaning. In certain embodiments, the semantic analysis 228 cognitiveskill may include processing a target sentence to parse it into itsindividual parts of speech, tag sentence elements that are related tocertain items of interest, identify dependencies between individualwords, and perform co-reference resolution. For example, if a sentencestates that the author really enjoys the hamburgers served by aparticular restaurant, then the name of the “particular restaurant” isco-referenced to “hamburgers.”

As likewise used herein, goal optimization broadly refers to performingmulti-criteria decision making operations to achieve a given goal ortarget objective. In certain embodiments, one or more goal optimization230 cognitive skills may be orchestrated by the cognitive compositionplatform 122 to generate a cognitive agent 250 for definingpredetermined goals, which in turn contribute to the generation of anassociated cognitive insight 262. For example, goals for planning avacation trip may include low cost (e.g., transportation andaccommodations), location (e.g., by the beach), and speed (e.g., shorttravel time). In this example, it will be appreciated that certain goalsmay be in conflict with another. As a result, a cognitive insight 262provided by the AIS 118 to a traveler may indicate that hotelaccommodations by a beach may cost more than they care to spend.

Collaborative filtering, as used herein, broadly refers to the processof filtering for information or patterns through the collaborativeinvolvement of multiple cognitive agents, viewpoints, data sources, andso forth. In certain embodiments, the application of such collaborativefiltering 232 cognitive skills may involve very large and differentkinds of data sets, including sensing and monitoring data, financialdata, and user data of various kinds. In certain embodiments,collaborative filtering may also refer to the process of makingautomatic predictions associated with predetermined interests of a userby collecting preferences or other information from many users. Forexample, if person ‘A’ has the same opinion as a person ‘B’ for a givenissue ‘x’, then an assertion can be made that person ‘A’ is more likelyto have the same opinion as person ‘B’ opinion on a different issue ‘y’than to have the same opinion on issue ‘y’ as a randomly chosen person.In certain embodiments, the collaborative filtering 206 cognitive skillmay be implemented with various recommendation engines familiar to thoseof skill in the art to make recommendations.

As used herein, common sense reasoning broadly refers to simulating thehuman ability to make deductions from common facts they inherently know.Such deductions may be made from inherent knowledge about the physicalproperties, purpose, intentions and possible behavior of ordinarythings, such as people, animals, objects, devices, and so on. In variousembodiments, certain common sense reasoning 234 cognitive skills may becomposed by the cognitive agent composition platform 120 to generate acognitive agent 250 that assists the AIS 118 in understanding anddisambiguating words within a predetermined context. In certainembodiments, the common sense reasoning 234 cognitive skill may be usedby the cognitive agent composition platform 120 to generate a cognitiveagent 250 that allows the AIS 118 to generate text or phrases related toa target word or phrase to perform deeper searches for the same terms.It will be appreciated that if the context of a word is betterunderstood, then a common sense understanding of the word can then beused to assist in finding better or more accurate information. Incertain embodiments, the better or more accurate understanding of thecontext of a word, and its related information, allows the AILS 118 tomake more accurate deductions, which are in turn used to generatecognitive insights 262.

As likewise used herein, natural language processing (NLP) broadlyrefers to interactions with a system, such as the AIS 118, through theuse of human, or natural, languages. In certain embodiments, various NLP210 cognitive skills may be implemented by the AIS 118 to achievenatural language understanding, which enables it to not only derivemeaning from human or natural language input, but to also generatenatural language output.

Summarization, as used herein, broadly refers to processing a set ofinformation, organizing and ranking it, and then generating acorresponding summary. As an example, a news article may be processed toidentify its primary topic and associated observations, which are thenextracted, ranked, and presented to the user. As another example, pageranking operations may be performed on the same news article to identifyindividual sentences, rank them, order them, and determine which of thesentences are most impactful in describing the article and its content.As yet another example, a structured data record, such as a patient'selectronic medical record (EMR), may be processed using certainsummarization 238 cognitive skills to generate sentences and phrasesthat describes the content of the EMR. In certain embodiments, varioussummarization 238 cognitive skills may be used by the cognitive agentcomposition platform 120 to generate to generate a cognitive agent 250that provides summarizations of content streams, which are in turn usedby the AIS 118 to generate cognitive insights 262.

As used herein, temporal/spatial reasoning broadly refers to reasoningbased upon qualitative abstractions of temporal and spatial aspects ofcommon sense knowledge, described in greater detail herein. For example,it is not uncommon for a particular set of data to change over time.Likewise, other attributes, such as its associated metadata, may alsochange over time. As a result, these changes may affect the context ofthe data. To further the example, the context of asking someone whatthey believe they should be doing at 3:00 in the afternoon during theworkday while they are at work may be quite different than asking thesame user the same question at 3:00 on a Sunday afternoon when they areat home. In certain embodiments, various temporal/spatial reasoning 214cognitive skills may be used by the cognitive agent composition platform120 to generate a cognitive agent 250 for determining the context ofqueries, and associated data, which are in turn used by the AIS 118 togenerate cognitive insights 262.

As likewise used herein, entity resolution broadly refers to the processof finding elements in a set of data that refer to the same entityacross different data sources (e.g., structured, non-structured,streams, devices, etc.), where the target entity does not share a commonidentifier. In certain embodiments, various entity resolution 216cognitive skills may be used by the cognitive agent composition platform120 to generate a cognitive agent 250 that can be used to identifysignificant nouns, adjectives, phrases or sentence elements thatrepresent various predetermined entities within one or more domains.From the foregoing, it will be appreciated that the generation of one ormore of the semantic analysis 228, goal optimization 230, collaborativefiltering 232, common sense reasoning 234, natural language processing236, summarization 238, temporal/spatial reasoning 240, and entityresolution 240 cognitive skills by the cognitive process orchestrationplatform 126 can facilitate the generation of a semantic, cognitivemodel.

In certain embodiments, the AIS 118 may receive public 202, proprietary204, transaction, social 208, device 210, and ambient 212 data, or acombination thereof, which is then processed by the AIS 118 to generateone or more cognitive graphs 230. As used herein, public 202 databroadly refers to any data that is generally available for consumptionby an entity, whether provided for free or at a cost. As likewise usedherein, proprietary 204 data broadly refers to data that is owned,controlled, or a combination thereof, by an individual user, group, ororganization, which is deemed important enough that it gives competitiveadvantage to that individual or organization. In certain embodiments,the organization may be a governmental, non-profit, academic or socialentity, a manufacturer, a wholesaler, a retailer, a service provider, anoperator of an AIS 118, and others. In certain embodiments, the publicdata 202 and proprietary 204 data may include structured,semi-structured, or unstructured data.

As used herein, transaction 206 data broadly refers to data describingan event, and is usually described with verbs. In typical usage,transaction data includes a time dimension, a numerical value, andcertain reference data, such as references to one or more objects. Incertain embodiments, the transaction 206 data may include credit ordebit card transaction data, financial services data of all kinds (e.g.,mortgages, insurance policies, stock transfers, etc.), purchase orderdata, invoice data, shipping data, receipt data, or any combinationthereof. In certain embodiments, the transaction data 206 may includeblockchain-associated data, smart contract data, or any combinationthereof. Skilled practitioners of the art will realize that many suchexamples of transaction 206 data are possible. Accordingly, theforegoing is not intended to limit the spirit, scope or intent of theinvention.

As used herein, social 208 data broadly refers to information thatsocial media users publicly share, which may include metadata such asthe user's location, language spoken, biographical, demographic orsocio-economic information, and shared links. As likewise used herein,device 210 data broadly refers to data associated with, or generated by,an apparatus. Examples of device 210 data include data associated with,or generated by, a vehicle, home appliance, security systems, and soforth, that contain electronics, software, sensors, actuators, andconnectivity, or a combination thereof, that allow the collection,interaction and provision of associated data.

As used herein, ambient 212 data broadly refers to input signals, orother data streams, that may contain data providing additional insightor context to public 202, proprietary 204, transaction 206, social 208,and device 210 data received by the AIS 118. For example, ambientsignals may allow the AIS 118 to understand that a user is currentlyusing their mobile device, at location ‘x’, at time ‘y’, doing activity‘z’. To continue the example, there is a difference between the userusing their mobile device while they are on an airplane versus usingtheir mobile device after landing at an airport and walking between oneterminal and another.

To extend the example, ambient 212 data may add additional context, suchas the user is in the middle of a three leg trip and has two hoursbefore their next flight. Further, they may be in terminal A1, but theirnext flight is out of C1, it is lunchtime, and they want to know thebest place to eat. Given the available time the user has, their currentlocation, restaurants that are proximate to their predicted route, andother factors such as food preferences, the AIS 118 can perform variouscognitive operations and provide a cognitive insight 262 that includes arecommendation for where the user can eat.

To extend the example even further, the user may receive a notificationwhile they are eating lunch at a recommended restaurant that their nextflight has been canceled due to the previously-scheduled aircraft beinggrounded. As a result, the user may receive two cognitive insights 262suggesting alternative flights on other carriers. The first cognitiveinsight 262 may be related to a flight that leaves within a half hour.The second cognitive insight 262 may be related to a second flight thatleaves in an hour but requires immediate booking and payment ofadditional fees. Knowing that they would be unable to make the firstflight in time, the user elects to use the second cognitive insight 262to automatically book the flight and pay the additional fees through theuse of a digital currency transaction.

In certain embodiments, the AIS 118 may be implemented to representknowledge in the cognitive graph 260, such that the knowledge can beused to perform reasoning and inference operations. In certainembodiments, the resulting reasoning and inference operations may beimplemented to provide self-assurance. Accordingly, such approaches maybe implemented in certain embodiments as a cognitive inference andlearning system (CILS). In certain embodiments, the self-assuranceresulting from such reasoning and inference operations may beimplemented to provide cognitive insights 262 with associatedexplainability. In these embodiments, such explainability may beimplemented to provide a rationale for their associated cognitiveinsights 262. As used herein, as it relates to explainability, describedin greater detail herein, rationale broadly refers to an explanation ofthe basis, or the set of reasons, or a combination thereof, used togenerate a particular cognitive insight 262.

As used herein, a cognitive graph 260 refers to a representation ofexpert knowledge, associated with individuals and groups over a periodof time, to depict relationships between people, places, and thingsusing words, ideas, audio and images. As such, it is a machine-readableformalism for knowledge representation that provides a common frameworkallowing data and knowledge to be shared and reused across user,application, organization, and community boundaries. In variousembodiments, the information contained in, and referenced by, acognitive graph 260 may be derived from many sources, such as public202, proprietary 204, transaction, social 208, device 210, and ambient212 data, or a combination thereof. In certain of these embodiments, thecognitive graph 260 may be implemented to assist in the identificationand organization of information associated with how people, places andthings are related to one other. In various embodiments, the cognitivegraph 260 may be implemented to enable automated cognitive agents 250,described in greater detail herein, to access the Web moreintelligently, enumerate inferences through utilization of various datasources, and provide answers to questions by serving as a computationalknowledge engine.

In certain embodiments, the cognitive graph 260 may be implemented tonot only elicit and map expert knowledge by deriving associations fromdata, but to also render higher level insights and accounts forknowledge creation through collaborative knowledge modeling. In certainembodiments, the cognitive graph 260 may be implements as amachine-readable, declarative memory system that stores and learns bothepisodic memory (e.g., specific personal experiences associated with anindividual or entity), and semantic memory, which stores factualinformation (e.g., geo location of an airport or restaurant).

For example, the cognitive graph 260 may know that a given airport is aplace, and that there is a list of related places such as hotels,restaurants and departure gates. Furthermore, the cognitive graph 260may know that people such as business travelers, families and collegestudents use the airport to board flights from various carriers, eat atvarious restaurants, or shop at certain retail stores. The cognitivegraph 260 may also have knowledge about the key attributes from variousretail rating sites that travelers have used to describe the food andtheir experience at various venues in the airport over the past sixmonths.

In various embodiments, the cognitive process orchestration platform 126may be implemented to orchestrate certain cognitive agents 250 togenerate one or more cognitive insights 262. In certain embodiments, theresulting cognitive insights 262 may be delivered to one or moredestinations 264, described in greater detail herein. As used herein, acognitive insight 262 broadly refers to an actionable, real-timerecommendation tailored to a particular user, as described in greaterdetail herein. Examples of such recommendations include getting animmunization, correcting a billing error, taking a bus to anappointment, considering the purchase of a particular item, selecting arecipe, eating a particular food item, and so forth.

In certain embodiments, cognitive insights 262 may be generated fromvarious data sources, such as public 202, proprietary 204, transaction,social 208, device 210, and ambient 212 data, a cognitive graph 260, ora combination thereof. For example, if a certain percentage of thepopulation in a user's community is suffering from the flu, then theuser may receive a recommendation to get a flu shot. In this example,determining the afflicted percentage of the population, or determininghow to define the community itself, may prove challenging. Accordingly,generating meaningful insights or recommendations may be difficult foran individual user, especially when related datasets are large.

In certain embodiments, a resulting cognitive insight 262 stream may beimplemented to be bidirectional, supporting flows of information bothtoo and from various destinations 264. In these embodiments, a firstflow of cognitive insights 262 may be generated in response to receivinga query, and subsequently delivered to one or more destinations 264.Likewise, a second flow of cognitive insights 262 may be generated inresponse to detecting information about a user of one or more of thedestinations 264.

Such use may result in the provision of information to the AIS 118. Inresponse, the AIS 118 may process that information, in the context ofwhat it knows about the user, and provide additional information to theuser, such as a recommendation. In certain embodiments, a stream ofcognitive insights 262 may be configured to be provided in a “push”stream configuration familiar to those of skill in the art. In certainembodiments, a stream of cognitive insights 262 may be implemented touse natural language approaches familiar to skilled practitioners of theart to support interactions with a user.

In certain embodiments, a stream of cognitive insights 262 may beimplemented to include a stream of visualized insights. As used herein,visualized insights broadly refer to cognitive insights that arepresented in a visual manner, such as a map, an infographic, images, andso forth. In certain embodiments, these visualized insights may includevarious cognitive insights, such as “What happened?”, “What do I knowabout it?”, “What is likely to happen next?”, or “What should I do aboutit?” In these embodiments, the stream of cognitive insights 262 may begenerated by various cognitive agents 250, which are applied to varioussources, datasets, and cognitive graphs.

In certain embodiments, the AIS 118 may be implemented to deliverCognition as a Service (CaaS). As such, it provides a cloud-baseddevelopment and execution platform that allow various cognitiveapplications and services to function more intelligently andintuitively. In certain embodiments, cognitive applications powered bythe AIS 118 are able to think and interact with users as intelligentvirtual assistants. As a result, users are able to interact with suchcognitive applications by asking them questions and giving themcommands. In response, these cognitive applications will be able toassist the user in completing tasks and managing their work moreefficiently.

In these and other embodiments, the AIS 118 may be implemented tooperate as an analytics platform to process big data, and dark data aswell, to provide data analytics through a public, private or hybridcloud environment, described in greater detail herein. As used herein,cloud analytics broadly refers to a service model wherein data sources,data models, processing applications, computing power, analytic models,and sharing or storage of results are implemented within a cloudenvironment to perform one or more aspects of analytics.

In certain embodiments, users may submit queries and computationrequests in a natural language format to the AIS 118. In response, theyare provided with a ranked list of relevant answers and aggregatedinformation with useful links and pertinent visualizations through agraphical representation. In these embodiments, the cognitive graph 230may be implemented to generate semantic and temporal maps to reflect theorganization of unstructured data and to facilitate meaningful learningfrom potentially millions of lines of text, much in the same way asarbitrary syllables strung together create meaning through the conceptof language.

In certain embodiments, the AIS 118 may be implemented to representknowledge in the cognitive graph 260, such that the knowledge can beused to perform reasoning and inference operations. In certainembodiments, the resulting reasoning and inference operations may beimplemented to provide self-assurance. Accordingly, such approaches maybe implemented in certain embodiments as a cognitive inference andlearning system (CILS). In certain embodiments, the self-assuranceresulting from such reasoning and inference operations may beimplemented to provide cognitive insights with associatedexplainability. In these embodiments, such explainability may beimplemented to provide a rationale for their associated cognitiveinsights.

FIG. 3 is a simplified block diagram of an augmented intelligence system(AIS) reference model implemented in accordance with an embodiment ofthe invention. In various embodiments, the AIS 118 reference model shownin FIG. 3 may be implemented as a reference for certain componentsincluded in, and functionalities performed by, an AIS 118, described ingreater detail herein. In certain embodiments, these components andfunctionalities may include a cognitive infrastructure 302, one or morecognitive Application Program Interfaces (APIs) 308, a cognitive processfoundation 310, various cognitive processes 320, and various cognitiveinteractions 328. In certain embodiments, the cognitive infrastructure302 may include various sources of multi-structured big data 304 and ahosted/private/hybrid cloud infrastructure, both of which are describedin greater detail herein.

In certain embodiments, the cognitive process foundation 310, likewisedescribed in greater detail herein, may be implemented to providevarious cognitive computing functionalities. In certain embodiments,these cognitive computing functionalities may include the simplificationof data and compute resource access 312. In certain embodiments, thesecognitive computing functionalities may likewise include various sharingand control 314 operations commonly associated with domain processes.Likewise, in certain embodiments these cognitive computingfunctionalities may include the composition and orchestration 316 ofvarious artificial intelligence (AI) systems.

In certain embodiments, the composition and orchestration 316 of variousartificial intelligence (AI) systems may include the composition ofcognitive skills and cognitive agents, as described in greater detailherein. In certain embodiments, the composition and orchestration 316may include the orchestration of various cognitive agents, andassociated AIS components, to generate one or more cognitive processes,likewise described in greater detail herein. In certain embodiments,these cognitive computing functionalities may include AI governance andassurance 318 operations associated with ensuring the integrity andtransparency of an AI system in the context of various cognitivecomputing operations it may perform.

As used herein, AI governance broadly refers to the management of theavailability, consistency, integrity, usability, security, privacy, andcompliance of data and processes used to perform a cognitive computingoperation. Certain embodiments of the invention reflect an appreciationthat practices and processes associated with AI governance ideallyprovide an effective foundation, strategy, and framework to ensure thatdata can be managed as an asset and transformed into meaningfulinformation as a result of a cognitive computing operation. Certainaspects of the invention likewise reflect an appreciation thatimplementation of various AI governance programs may include a governingbody or council, a defined set of procedures, and a plan to executethose procedures.

Certain embodiments of the invention reflect an appreciation that AIgovernance typically includes other concepts, such as data stewardshipand data quality, which may be used to improve control over variouscomponents of an AIS 118. In various embodiments, certain AI governance318 operations may be implemented to improve control over othercomponents of the cognitive process foundation 310. In theseembodiments, the AI governance and assurance 318 operations may beimplemented to improve control over the simplification of data andcompute access 312, sharing and controlling domain processes 314, andcomposing and orchestrating AI systems 316.

In certain embodiments, the AI governance and assurance 318 operationsmay likewise be implemented to improve control over a cognitiveinfrastructure 302, cognitive APIs 308, cognitive processes 320, andcognitive interactions 328. Various embodiments of the inventionlikewise reflect an appreciation that improving control over suchcomponents of an AIS 118 may include certain methods, technologies, andbehaviors, described in greater detail herein. Likewise, variousembodiments of the invention reflect an appreciation that effective AIgovernance generally involves the exercise of authority and control(e.g., planning, monitoring, enforcement, etc.) over the management ofAIS 118 components used in the performance of certain cognitivecomputing operation.

Furthermore, certain embodiments of the invention reflect anappreciation that the lack of adequate AI governance may result in poordata quality. Moreover, various embodiments of the invention reflect anappreciation that the lack of adequate AI governance may result in poor,unexpected, or otherwise undesirable performance of certain cognitiveskills and cognitive agents, described in greater detail herein.Accordingly, certain embodiments of the invention likewise reflect anappreciation that poor data quality, unexpected, or otherwiseundesirable performance of certain cognitive skills and cognitiveagents, or a combination thereof, may have an adverse effect upon theresults of an associated cognitive computing operation.

As likewise used herein, AI assurance broadly refers to ensuring thetransparency, interpretability, impartiality, accountability, andtrustworthiness of the cognitive computing operations an AIS 118performs to produce a resulting outcome, such as a cognitive insight.Certain embodiments of the invention reflect an appreciation thatpractices and processes associated with AI assurance generally providean effective foundation, strategy, and framework to ensure that an AIS118 can perform its intended function free from deliberate orinadvertent manipulation. Certain embodiments of the invention reflectan appreciation that such practices and processes can likewise assist inensuring cognitive computing operations performed by an AIS 118 adhereto its operational and technical parameters within prescribed limits. Incertain embodiments, various cognitive computing functionalities may beimplemented to work individually, or in concert with one another. Inthese embodiments, the method by which these various cognitive computingfunctionalities is a matter of design choice.

As used herein, a cognitive process broadly refers to an instantiationof a cognitive computing operation, described in greater detail herein.In certain embodiments, the cognitive process 320 may be implemented asan intelligent user engagement 322. As used herein, an intelligent userengagement 322 broadly refers to the application of certain cognitiveoperations to assist in more meaningful cognitive interactions 328between a user and certain cognitive processes 320. In certainembodiments, the cognitive process 320 may be implemented as anaugmented process engagement 324. As used herein, an augmented processengagement broadly refers to the application of cognitive operations toimprove interaction between certain cognitive processes 320. In certainembodiments, the cognitive process 320 may be implemented as one or morecognitive applications 326, described in greater detail herein. Incertain embodiments, cognitive interactions 328 may be implemented tosupport user interactions with an AIS 118 through web 330 applications,mobile 332 applications, chatbot 334 interactions, voice 336interactions, augmented reality (AR) and virtual reality (VR)interactions 338, or a combination thereof.

FIG. 4 is a simplified block diagram of an augmented intelligence system(AIS) platform implemented in accordance with an embodiment of theinvention. In certain embodiments, the AIS platform may be implementedto include various cognitive processes 320, a cognitive processfoundation 310, various Cognitive Application Program Interfaces (APIs)308, and an associated cognitive infrastructure 302. In certainembodiments, the cognitive processes 320 may be implemented tounderstand and adapt to the user, not the other way around, by nativelyaccepting and understanding human forms of communication, such asnatural language text, audio, images, video, and so forth.

In these and other embodiments, the cognitive processes 320 may beimplemented to possess situational and temporal awareness based uponambient signals from users and data, which facilitates understanding theuser's intent, content, context and meaning to drive goal-driven dialogsand outcomes. Further, they may be designed to gain knowledge over timefrom a wide variety of structured, non-structured, transactional, anddevice data sources, continuously interpreting and autonomouslyreprogramming themselves to better understand a given domain. As such,they are well-suited to support human decision making, by proactivelyproviding trusted advice, offers and recommendations while respectinguser privacy and permissions.

In certain embodiments, the cognitive processes 320 may be implementedin concert with a cognitive application framework 442. In certainembodiments, the cognitive processes 320 and the cognitive applicationframework 442 may be implemented to support plug-ins and components thatfacilitate the creation of various cognitive applications 326, describedin greater detail herein. In certain embodiments, the cognitiveprocesses 320 may be implemented to include widgets, user interface (UI)components, reports, charts, and back-end integration componentsfamiliar to those of skill in the art.

As likewise shown in FIG. 4 , the cognitive process foundation 310 maybe implemented in certain embodiments to include a cognitive processorchestration platform 126, a cognitive process composition platform122, and various cognitive agents 250, all of which are described ingreater detail herein. In certain embodiments, the cognitiveorchestration platform 126 may be implemented to orchestrate variouscognitive agents 250 to enable one or more cognitive processes 320. Incertain embodiments, the cognitive orchestration platform 126 may beimplemented to manage accounts and projects, along with user-specificmetadata that is used to drive processes and operations within thecognitive process foundation 310 for a particular project.

In certain embodiments, the cognitive agent composition platform 120 maybe implemented to compose one or more cognitive agents 250. In certainembodiments, the cognitive agents 250 may include a sourcing agent 432,a destination agent 434, an engagement agent 436, a compliance agent438, or a combination thereof. In certain embodiments, the sourcingagent 432 may be implemented to source a variety of multi-site,multi-structured source streams of data described in greater detailherein.

In various embodiments, the destination agent 436 may be implemented topublish cognitive insights to a consumer of cognitive insight data.Examples of such consumers of cognitive insight data include targetdatabases, public or private blockchains, business intelligenceapplications, and mobile applications. It will be appreciated that manysuch examples of cognitive insight data consumers are possible. Incertain embodiments, the engagement agents 436 may be implemented todefine various cognitive interactions 328 between a user and aparticular cognitive process 320. In certain embodiments, the complianceagents 438 may be implemented to ensure compliance with certain businessand technical guidelines, rules, regulations or other parametersassociated with an organization.

In certain embodiments, the resulting cognitive agents 250 may beorchestrated by the cognitive process orchestration platform 126 tocreate cognitive insights, described in greater detail herein. Incertain embodiments, the resulting cognitive agents 250 may beorchestrated by the cognitive process orchestration platform 126 tocreate custom extensions to the AIS 118 shown in FIG. 2 . In certainembodiments, the cognitive process foundation 310 may be implemented forthe development of a cognitive application 326, which may subsequentlybe deployed in a public, private or hybrid cloud 306 cloud environment.

In various embodiments, the APIs 308 may be implemented for use by thecognitive process orchestration platform 126 to orchestrate certaincognitive agents 250, described in greater detail herein, which are thenexecuted by the AIS 118 to generate cognitive insights. In certainembodiments, the APIs 308 may be implemented to access various cognitiveinfrastructure 302 components. In certain embodiments, theinfrastructure components may include repositories of multi-structuredbig data 304, a hosted/private/hybrid cloud 306 environment, or both. Incertain embodiments, the repositories of multi-structured big data 304may be accessed by the AIS platform to generate cognitive insights.

In certain embodiments, the repositories of multi-structured big data304 may include individual repositories of public 202, proprietary 204,transaction 206, social 208, device 210, and ambient 212 data, or somecombination thereof. In certain embodiments, the repositories oftransaction data 206 may include blockchain data associated with one ormore public blockchains, one or more private blockchains, or acombination thereof. In certain embodiments, the repositories oftransaction data 206 may be used to generate a blockchain-associatedcognitive insight.

In certain embodiments, as described in greater detail herein, thecognitive infrastructure 302 environment may include variousinput/output services 404, described in greater detail herein, acognitive cloud management 406 platform, and various cognitive cloudanalytics 408 components, or a combination thereof. In certainembodiments, hosted/private/hybrid cloud 306 may include a repository ofcognitive process components 402. In certain embodiments, the repositoryof cognitive process components 402 may be implemented to storecognitive agents, cognitive skills, cognitive models, cognitivealgorithms, and cognitive actions.

In various embodiments, the contents of the cognitive process components402 may be used by the cognitive skill composition platform 122 tocompose certain cognitive skills. In various embodiments, the contentsof the cognitive process components 402 may be used by the cognitiveagent composition platform 120 to compose certain cognitive agents. Invarious embodiments, the contents of the cognitive process components402 may be used by the cognitive process orchestration platform 126 toorchestrate certain cognitive processes 320.

FIG. 5 shows a simplified block diagram of components associated with acognitive process foundation implemented in accordance with anembodiment of the invention. In certain embodiments, the cognitiveprocess foundation 310 may be implemented to include an augmentedintelligence system (AIS) composition platform 120 and a cognitiveprocess orchestration platform 126. In certain embodiments, thecognitive agent composition platform 120 may be implemented to includean AIS composition user interface (UI) 522, a cognitive skillcomposition platform 122, and a cognitive agent composition platform124, all of which are described in greater detail herein. In certainembodiments, the cognitive composition UI 522 may be implemented toreceive user input, and provide a visual representation of the executionof individual operations, associated with the cognitive skillcomposition 122 and cognitive agent composition 124 platforms. Incertain embodiments, the AIS composition UI 522 may be implemented as aGraphical User Interface (GUI).

In various embodiments, the cognitive skill composition platform 122 maybe implemented to perform certain cognitive skill composition 530operations associated with the composition of a particular cognitiveskill. In certain embodiments, the cognitive skill composition 530operations may include the development, testing, and definition of acognitive skill, as described in greater detail herein. In certainembodiments, the cognitive skill composition 530 operations may includethe development of one or more cognitive algorithms, as likewisedescribed in greater detail herein. In certain embodiments, thecognitive skill composition 530 operations may include the definition ofvarious cognitive model actions. In certain embodiments, the cognitiveskill composition 530 operations may include the identification of datasources, such as the public 202, proprietary 204, transaction, social208, device 210, and ambient 212 data sources described in thedescriptive text associated with FIG. 2 . In certain embodiments, thecognitive skill composition 530 operations may include the definition ofrequired datasets, described in greater detail herein.

In certain embodiments, the cognitive skill composition platform 122 maybe implemented with an associated cognitive skill client library 540 andone or more cognitive skill Application Program Interfaces (APIs) 550.In certain embodiments, the cognitive skill client library 540, and oneor more cognitive skill Application Program Interfaces (APIs) 550, maybe implemented by the cognitive skill composition platform 122 tocompose a particular cognitive skill.

In various embodiments, the cognitive agent composition platform 124 maybe implemented to perform certain cognitive agent composition 532operations associated with the composition of a particular cognitiveskill. In certain embodiments, the cognitive agent composition 532operations may include the development of various datasets used by aparticular cognitive agent during its execution. In various embodiments,the cognitive agent composition 532 operations may include the curationand uploading of certain training data used by a cognitive modelassociated with a particular cognitive agent. In certain embodiments,the development of the various datasets and the curation and uploadingof certain training data may be performed via a data engineeringoperation.

In certain embodiments, the cognitive agent composition 532 operationsmay include creation of a cognitive agent record. In certainembodiments, the cognitive agent record may be implemented by an AIS totrack a particular cognitive agent. In certain embodiments, thecognitive agent record may be implemented by an AIS to locate andretrieve a particular cognitive agent stored in a repository of AIScomponents, described in greater detail herein. In certain embodiments,the cognitive agent composition 532 operations may include the addition,and configuration of, one or more cognitive skills associated with aparticular cognitive agent.

In certain embodiments, the cognitive agent composition 532 operationsmay include the definition of various input/output services, describedin greater detail herein, associated with a particular cognitive agent.In certain embodiments, the cognitive agent composition 532 operationsmay include the definition of various dataset connections associatedwith a particular cognitive agent. In certain embodiments, thedefinition of various dataset connections may be performed via a dataengineering operation. In certain embodiments, the cognitive agentcomposition 532 operations may include the creation of one or more dataflows associated with a particular cognitive agent. In certainembodiments, the cognitive agent composition 532 operations may includethe mapping of one or more data flows associated with a particularcognitive agent. In certain embodiments, the mapping of data flows maybe performed via a date engineering operation. In certain embodiments,the cognitive agent composition 532 operations may include the testingof various services associated with a particular cognitive agent.

In certain embodiments, the cognitive agent composition platform 120 maybe implemented with an associated cognitive agent client library 542 andone or more cognitive agent APIs 552. In certain embodiments, thecognitive agent library 542, and one or more cognitive agent APIs 552,may be implemented by the cognitive agent composition platform 120 tocompose a particular cognitive agent.

In certain embodiments, the cognitive process foundation 310 may beimplemented to include a cognitive process orchestration platform 126.In certain embodiments, the cognitive process orchestration platform 126may be implemented to include an AIS administration console 524, an AIScommand line interface (CLI) 526, or both. In certain embodiments, theAIS administration console 524 may be implemented as a GUI.

In certain embodiments, the AIS administration console 524 and the AISCLI 526, individually or in combination, may be implemented to managethe building blocks of a particular cognitive process, described ingreater detail herein. In certain embodiments, the building blocks of aparticular cognitive process may include one or more cognitive agents,likewise described in greater detail herein. In certain embodiments, theAIS administration console 524 and the AIS CLI 526, individually or incombination, may be implemented to manage lifecycle of a cognitiveagent, described in greater detail herein.

In certain embodiments, the AIS administration console 524 may beimplemented to manage a cognitive process user account. In certainembodiments, the AIS administration console 524 may be implemented viewvarious AIS logs and metrics. In certain embodiments, the AISadministration console 524 may be implemented as a web interfacefamiliar to those of skill in the art.

In certain embodiments, the AIS CLI 526 may be implemented to generateand deploy cognitive skills, created and save dataset definitions,invoke cognitive agent services, and configure cognitive action batchjobs and connections, or a combination thereof. In certain embodiments,the AIS CLI 526 may be implemented to add cognitive agent buildingblocks to the cognitive agent composition platform 120. In certainembodiments, the AIS CLI 526 may be implemented to execute cognitiveagent lifecycle commands.

In certain embodiments, the AIS administration console 524 and the AISCLI 526, individually or in combination, may be implemented to performvarious data orchestration 534 operations. In certain embodiments, thedata orchestration 534 operations may include the definition of datasources associated with a particular AIS region, described in greaterdetail herein. In certain embodiments, the data orchestration 534operations may include the definition of various data variablesassociated with a particular AIS region.

In certain embodiments, the AIS administration console 524 and the AISCLI 526, individually or in combination, may be implemented to performvarious cognitive agent orchestration 536 operations. In certainembodiments, the cognitive agent orchestration 536 operations mayinclude the creation of a cognitive agent snapshot. As used herein, acognitive agent snapshot broadly refers to a depiction of theoperational state of a cognitive agent at a particular instance in timeduring the execution of a cognitive process.

In certain embodiments, the cognitive agent orchestration 536 operationsmay include the promotion of a cognitive agent snapshot. As likewiseused herein, promotion broadly refers to the transition of a cognitiveagent, or a cognitive process, from one operational environment toanother. As an example, the cognitive process orchestration platform 126may be implemented in a development environment to generate a cognitiveprocess by orchestrating certain cognitive agents, as described ingreater detail herein. Once development has been completed, theresulting cognitive process may be promoted to a test environment.Thereafter, once testing of the cognitive process has been completed, itmay be promoted to a user acceptance environment, and once the useracceptance phase has been completed, it may be promoted to a productionenvironment.

In certain embodiments, the cognitive agent orchestration 536 operationsmay include the creation of a cognitive agent instance. In certainembodiments, the cognitive agent orchestration 536 operations mayinclude enablement of start triggers for a particular cognitive agent.In certain embodiments, the cognitive agent orchestration 536 operationsmay include the invocation of a particular instance of a cognitiveagent. In certain embodiments, the cognitive agent orchestration 536operations may include querying and filtering responses received from aparticular cognitive agent. In certain embodiments, the cognitiveprocess orchestration platform 126 may be implemented with an associatedAIS console client library 544, one or more AIS console APIs 554, an AISCLI client library 546, one or more AIS CLI APIs 556, or a combinationthereof.

FIG. 6 is a simplified block diagram of a plurality of augmentedintelligence system (AIS) platforms implemented in accordance with anembodiment of the invention within a hybrid cloud infrastructure. Incertain embodiments, the hybrid cloud infrastructure 304 may beimplemented to include a cognitive cloud management 402 platform, ahosted 602 cognitive cloud environment, and a private 622 cognitivecloud environment. In certain embodiments, the private 622 cognitivecloud environment may be implemented in a private network, such ascommonly implemented by corporation or government organization.

In certain embodiments, the hosted 602 and private 622 cognitive cloudenvironment may respectively be implemented to include a hosted 604 andprivate 624 AIS platform. Likewise, in certain embodiments, the hosted604 and private 624 AIS platforms may respectively be implemented toinclude one or more hosted 626 and private 626 AIS regions. As usedherein, a hosted 606 AIS region broadly refers to a designatedinstantiation of an AIS implemented to execute on a corresponding hosted604 platform. As likewise used herein, a private 626 AIS region broadlyrefers to a designated instantiation of an AIS implemented to execute ona corresponding private 624 platform. In certain embodiments, thedesignated instantiation of a hosted 606 or private 626 AIS region maybe defined by a set of associated parameters.

As an example, the designation parameters associated with a hosted 606or private 626 AIS region, individually or in combination, may tocorrespond to a defined geographic area. To continue the example, thedesignation parameters associated with a particular hosted 606 AISregion may correspond to certain defining information associated withthe state of Texas. Likewise, the designation parameters associated witha first and second private 626 AIS region may respectively correspond tocertain defining information associated with Dallas and Harris counties,both of which are located in the state of Texas. In this example, thehosted 606 AIS region may be implemented to provide various cognitiveinsights related to the state government of Texas to various countygovernments. Likewise, the first and second private 626 AIS regions maybe respectively implemented to provide cognitive insights specific tothe county governments of Dallas and Harris counties.

As another example, the designation parameters associated with a hosted606 or private 626 AIS region, individually or in combination, may tocorrespond to various aspects of an organization. To continue theexample, the designation parameters associated with a particular hosted606 AIS region may correspond to certain defining information associatedwith an automobile dealer network. Likewise, the designation parametersassociated with a first and second private 626 AIS region mayrespectively correspond to certain defining information associated withtwo independent automobile dealers, both of which are located in thesame city and sell the same brand of automobiles. In this example, thehosted 606 AIS region may be implemented to provide various cognitiveinsights related to certain aspects of the automobile brand. Likewise,the first and second private 626 AIS regions may be respectivelyimplemented to provide cognitive insights specific to certain aspects ofthe two automobile dealers, such as their respective inventories,customer demographics, and past promotional activities.

In certain embodiments, each hosted 606 and private 626 AIS regions maybe implemented to include one hosted 608 or private 628 AISenvironments. As used herein, a hosted 608 AIS environment broadlyrefers to an operating environment within which a particular hosted 608AIS environment is implemented. As likewise used herein, a privatehosted 628 AIS environment broadly refers to an operating environmentwithin which a particular private 628 AIS environment is implemented.

As an example, a cognitive process may first be implemented in a hosted608 or private 628 development environment to generate a cognitiveprocess by orchestrating certain cognitive agents, as described ingreater detail herein. Once development has been completed, theresulting cognitive process may be promoted to a hosted 608 or private628 test environment. Thereafter, once testing of the cognitive processhas been completed, it may be promoted to a hosted 608 or private 628user acceptance environment. Likewise, once the user acceptance phasehas been completed, it may be promoted to a hosted 608 or private 628production environment.

In certain embodiments, each hosted 608 and private 628 AIS environmentsmay be implemented to include they use of one or more hosted 610 orprivate 630 cognitive agents, described in greater detail herein, togenerate cognitive insights, likewise described in greater detailherein. In certain embodiments, a gateway/load balancer 644 may beimplemented to allow the hosted 604 and private 624 AIS platforms tocommunicate with one another. In certain embodiments, the ability tocommunicate with one another allows the hosted 604 and private 624 AISplatforms to work collaboratively when generating cognitive insightsdescribed in greater detail herein.

FIG. 7 shows components of a plurality of augmented intelligence system(AIS) platforms implemented in accordance with an embodiment of theinvention within a hosted/private/hybrid cloud environment. In certainembodiments, the hybrid cloud infrastructure 304 may be implemented toinclude a cognitive cloud management 406 platform, a hosted 702cognitive container management infrastructures, and a private 722cognitive container management infrastructure. In certain embodiments,the hosted 702 and private 722 cognitive container managementinfrastructures may be implemented to respective include one or morevirtual machines (VMs) ‘1’ 704 through ‘n’ 706 and VMs ‘1 724 through‘n’ 726.

In certain embodiments, the hybrid cloud infrastructure 304 may likewisebe implemented to include hosted 708 and private 728 cognitive servicesinfrastructures, hosted 716 and private 736 cognitive computeinfrastructures, and a gateway/load balancer 644. In certainembodiments, the hosted 708 and private 728 cognitive servicesinfrastructures may be implemented to respective include one or morevirtual machines (VMs) ‘1’ 710 through ‘n’ 712 and VMs ‘1 730 through‘n’ 732. In certain embodiments, the hosted 716 and private 736cognitive compute infrastructures may likewise be implemented torespective include one or more virtual machines (VMs) ‘1’ 718 through‘n’ 720 and VMs ‘1 738 through ‘n’ 740.

Likewise, in certain embodiments the hybrid cloud infrastructure 304 maybe implemented to include various repositories of hosted 714 and private734 data. As used herein, a repository of hosted 714 or private 734 databroadly refers to a collection of knowledge elements that can be used incertain embodiments to generate one or more cognitive insights,described in greater detail herein. In certain embodiments, theseknowledge elements may include facts (e.g., milk is a dairy product),information (e.g., an answer to a question), descriptions (e.g., thecolor of an automobile), abilities (e.g., the knowledge of how toinstall plumbing fixtures), and other classes of knowledge familiar tothose of skill in the art. In these embodiments, the knowledge elementsmay be explicit or implicit. As an example, the fact that water freezesat zero degrees centigrade is an explicit knowledge element, while thefact that an automobile mechanic knows how to repair an automobile is animplicit knowledge element.

In certain embodiments, the knowledge elements within a repository ofhosted 714 or private 734 data may also include statements, assertions,beliefs, perceptions, preferences, sentiments, attitudes or opinionsassociated with a person or a group. As an example, user ‘A’ may preferthe pizza served by a first restaurant, while user ‘B’ may prefer thepizza served by a second restaurant. Furthermore, both user ‘A’ and ‘B’are firmly of the opinion that the first and second restaurantsrespectively serve the very best pizza available. In this example, therespective preferences and opinions of users ‘A’ and ‘B’ regarding thefirst and second restaurant may be included in a universal knowledgerepository as they are not contradictory. Instead, they are simplyknowledge elements respectively associated with the two users and can beused in various embodiments for the generation of certain cognitiveinsights, as described in greater detail herein.

In certain embodiments, individual knowledge elements respectivelyassociated with the repositories of hosted 714 and private 734 data maybe distributed. In certain embodiments, the distributed knowledgeelements may be stored in a plurality of data stores familiar to skilledpractitioners of the art. In certain embodiments, distributed knowledgeelements may be logically unified for various implementations of therepositories of hosted 714 and private 734 data.

In certain embodiments, the repositories of hosted 714 and private 734data may be respectively implemented in the form of a hosted or privateuniversal cognitive graph, described in greater detail herein. Incertain embodiments, individual nodes within a hosted or privateuniversal cognitive graph may contain one or more knowledge elements. Incertain embodiments, the repositories of hosted 714 and private 734 datamay be respectively implemented to include a repository of hosted andprivate AIS components, such as the AIS component repository 402 shownin FIG. 4 and described in its associated descriptive text.

In certain embodiments, the repositories of hosted 714 and private 734data may respectively include one or more repositories of applicationdata, proprietary data, and proprietary transaction data. In certainembodiments, the repositories of hosted or private transaction data mayinclude credit or debit card transaction data, financial services dataof all kinds (e.g., mortgages, insurance policies, stock transfers,etc.), purchase order data, invoice data, shipping data, receipt data,or any combination thereof. In various embodiments, the repositories ofhosted or private transaction data may likewise includeblockchain-associated data, smart contract data, or any combinationthereof.

In certain embodiments, hosted and private transaction data may beexchanged through the implementation of a transaction data exchangeimplemented on the gateway/load balancer 644. In certain embodiments,the implementation of such a transaction data exchange may allow thehosted 716 cognitive compute infrastructure to access certain privatetransaction data. Conversely, the private 736 cognitive computeinfrastructure may be allowed to access certain hosted transaction data.In certain embodiments, the transaction data exchange may be implementedwith permission and identity management controls to determine the degreeto which certain hosted and private transaction data may be respectivelyaccessed by the hosted 716 and private 736 cognitive computeinfrastructures.

In certain embodiments, the repositories of hosted or privatetransaction data may include data associated with a public blockchain.As used herein, a public blockchain broadly refers to a blockchain thathas been implemented as a permissionless blockchain, meaning anyone canread or write to it. One advantage of such a public blockchain is itallows individuals who do not know each other to trust a shared recordof events without the involvement of an intermediary or third party.

In certain embodiments, a repository of private transaction data may beimplemented to include data associated with a proprietary blockchain. Aslikewise used herein, a proprietary blockchain broadly refers to ablockchain where its participants are known and are granted read andwrite permissions by an authority that governs the use of theblockchain. For example, proprietary blockchain participants may belongto the same or different organizations within an industry sector. Incertain embodiments, these relationships may be governed by informalrelationships, formal contracts, or confidentiality agreements.

Skilled practitioners of the art will recognize that while manytransactions may benefit from the decentralized approach typicallyimplemented by a public blockchain, others are more suited to beinghandled by an intermediary. Such intermediaries, while possibly addingadditional complexities and regulation, can often provide demonstrablevalue. In certain embodiments, an intermediary associated with aproprietary blockchain may have the ability to veto or rescind suspecttransactions, provide guarantees and indemnities, and deliver variousservices not generally available through a public blockchain.

Furthermore, proprietary blockchains have several advantages, includingthe use of cryptographic approaches known to those of skill in the artfor identity management and verification of transactions. Theseapproaches not only prevent the same transaction taking place twice,such as double-spending a digital currency, they also provide protectionagainst malicious activities intended to compromise a transaction bychanging its details. Moreover, permission controls typically associatedwith proprietary blockchains can provide dynamic control over who canconnect, send, receive and enact individual transactions, based upon anynumber of parameters that may not be available or implementable inpublic blockchains. Accordingly, full control can be asserted over everyaspect of a proprietary blockchain's operation, not only in accordancewith the consensus of its various participants, but its administrativeintermediary as well.

In certain embodiments, the hosted 708 or private 728 cognitive servicesinfrastructure may be implemented to manage the identity of a user,group or organization in the performance of blockchain-associatedcognitive insight operations. In certain embodiments, the hosted 708 orprivate 728 cognitive services infrastructure may be implemented toperform various cognitive identity management operations. In certainembodiments, the cognitive identity management operations may includethe use of cognitive personas, cognitive profiles, or a combinationthereof, to perform blockchain-associated cognitive insight operationsassociated with a particular user, group or organization. In certainembodiments, the cognitive identity management operations may beimplemented to verify the identity of a user, group or organization inthe performance of a blockchain-associated cognitive insight operation.

In certain embodiments, the cognitive identity management operations maylikewise involve the generation, and ongoing management of, privatekeys, shared keys, public/private key pairs, digital signatures, digitalcertificates, or any combination thereof, associated with a particularuser, group or organization. Likewise, in certain embodiments thecognitive identity management operations may involve the encryption ofone or more cognitive insights, one or more smart contracts, or somecombination thereof, during the generation of a blockchain-associatedcognitive insight. Those of skill in the art will recognize that manysuch embodiments are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

In various embodiments, the gateway/load balancer 644 may be implementedfor the hosted 708 cognitive services infrastructure provide certainhosted data and knowledge elements to the private 728 cognitive servicesinfrastructure, In certain embodiments, the provision of certain hosteddata and knowledge elements allows the hosted 714 repository of data tobe replicated as the private 724 repository of data. In certainembodiments, the provision of certain hosted data and knowledge elementsto the private 728 cognitive services infrastructure allows the hosted714 repository of data to provide updates to the private 734 repositoryof data. In certain embodiments, the updates to the private 734repository of data do not overwrite other data. Instead, the updates aresimply added to the private 734 repository of data.

In certain embodiments, knowledge elements and data that are added tothe private 734 repository of data are not respectively provided to thehosted 714 repository of data. As an example, an airline may not wish toshare private information related to its customer's flights, the pricepaid for tickets, their awards program status, and so forth. In variousembodiments, certain knowledge elements and data that are added to theprivate 724 repository of data may be provided to the hosted 714repository of data. As an example, the owner of the private 734repository of data may decide to license certain knowledge elements anddata to the owner of the hosted 714 repository of data. To continue theexample, certain knowledge elements or data stored in the private 734repository of data may be anonymized prior to being provided forinclusion in the hosted 714 repository of data.

In certain embodiments, only private knowledge elements or data arestored in the private 734 repository of data. In certain embodiments,the private 736 cognitive compute infrastructure may use knowledgeelements and data stored in both the hosted 714 and private 734repositories of data to generate cognitive insights. Skilledpractitioners of the art will recognize that many such embodiments arepossible. Accordingly, the foregoing is not intended to limit thespirit, scope or intent of the invention.

FIGS. 8 a and 8 b are a simplified process diagram showing theperformance of cognitive process promotion operations implemented inaccordance with an embodiment of the invention. In various embodiments,a cognitive process foundation 310, described in greater detail herein,may be implemented to perform certain cognitive process promotion 828operations associated with promoting a particular cognitive process fromone operating environment to another.

As shown in FIG. 8 a , certain operations may be performed in a datasourcing 804 phase, which results in the generation various data sets806, which are then used in a machine learning (ML) model development808 phase. In turn, the resulting ML model may be incorporated into oneor more cognitive skills 810, which are then used in a cognitive agentdevelopment 812 phase to generate various cognitive agents 814.

The resulting cognitive agents 814, as shown in FIG. 8 b , may then beimplemented in a cognitive agent deployment and management 816 phase,which results in certain feedback 818. In turn, the feedback 818 may beused in a cognitive agent measurement and performance 820 phase, whichresults in the generation of various cognitive insights 822. Theresulting cognitive insights 822 may then be used in a cognitive agentgovernance and assurance 826 phase. In various embodiments, thecognitive agent governance and assurance 826 phase may be implemented toperform certain cognitive agent governance and assurance operations. Invarious embodiments, the cognitive agent governance and assurance 826phase may be performed via a cognitive assurance agent.

In certain embodiments, the cognitive agent governance and assuranceoperations may include the provision of AIS explainability. As usedherein and as it relates to AI assurance, AIS explainability broadlyrefers to transparently conveying to a user the structural andoperational details of the ML model(s) used by an AIS, statistical andother descriptive properties of its associated training data, andvarious evaluation metrics from which its likely behavior may beinferred. In certain embodiments, the cognitive agent governance andassurance operations may include enforcement of a policy associated withthe enforcement of a particular cognitive insight. In these embodiments,the particulars associated with the policy, and the method by which itis enforced, is a matter of design choice.

In certain embodiments, the cognitive agent governance and assuranceoperations may include governance of the creation, and use, of aparticular cognitive model and the lineage of the data it may use. Inthese embodiments, the method by which the governance is defined, andthe method by which it is enforced, is a matter of design choice. Incertain embodiments, the cognitive agent governance and assuranceoperations may include providing assurance that intellectual property(IP) ownership rights are preserved. In certain embodiments, the IP maybe associated with certain cognitive operations, cognitive processes,cognitive skills, cognitive agents, and cognitive insights, or acombination thereof.

In certain embodiments, the cognitive agent governance and assuranceoperations may include KPI-driven AI model optimization. In theseembodiments, the definition of such KPIs, and the method by which theyare used to optimize a particular AI model, is a matter of designchoice. In certain embodiments, the cognitive agent governance andassurance operations may include the provisions of AI auditability. Incertain embodiments, AI auditability may include the ability to provideexplainability, and associated lineage, of how a particular cognitiveoperation, cognitive process, cognitive skill, cognitive agent, orcognitive insight, or a combination thereof, was generated andimplemented. In these embodiments, the method by which the AIauditability is achieved is a matter of design choice.

FIG. 9 is a simplified process diagram showing phases of a cognitiveprocess lifecycle implemented in accordance with an embodiment of theinvention. As used herein, a cognitive process lifecycle broadly refersto a series of phases, or individual operational steps, or a combinationthereof, associated with a cognitive process, spanning its inception,development, implementation, testing, acceptance, production, revision,and eventual retirement. In certain embodiments, each phase oroperational step of a cognitive process lifecycle may have associatedinput artifacts, roles or actors, and output artifacts.

As used herein, an input artifact broadly refers to an article ofinformation used to perform an operation associated with completion of acertain phase, or performance of certain operational steps, of acognitive process lifecycle. Examples of input artifacts includearticles of information related to business and technical ideas, goals,needs, structures, processes, and requests. Other examples of inputartifacts include articles of information related to market andtechnical constraints, system architectures, and use cases. Yet otherexamples of input artifacts include articles of information related todata sources, previously-developed technology components, andalgorithms.

As likewise used herein, a role or actor broadly refers to a particularuser, or certain functions they may perform, participating in certainphases or operational steps of a cognitive process lifecycle. Examplesof roles or actors include business owners, analysts, and partners, userexperience (UX) and user interface (UI) designers, and project managers,as well as solution, enterprise and business process architects, Otherexamples of roles or actors include data scientists, machine learning(ML) engineers, data, integration and software engineers, as well assystem administrators.

An output artifact, as likewise used herein, broadly refers to anarticle of information resulting from the completion of a certain phase,or performance of certain operational steps, of a cognitive processlifecycle. Examples of output artifacts includeStrength/Weaknesses/Opportunity/Threat (SWOT) analysis results, KeyPerformance Indicator (KPI) definitions, and project plans. Otherexamples of output artifacts include use case models and documents,cognitive application UX and UI designs, and project plans. Yet otherexamples of output artifacts include dataset, algorithm, machinelearning (ML) model, cognitive skill, and cognitive agentspecifications, as well as their corresponding datasets, algorithms, MLmodels, cognitive skills, and cognitive agents. Those of skill in theart will recognize that many examples of input artifacts, roles oractors, and output artifacts are possible. Accordingly, the foregoing isnot intended to limit the spirit, scope or intent of the invention.

In this embodiment, a cognitive process lifecycle is begun in step 902,followed by determining certain operational and performance parametersrelated to an associated cognitive process in step 904. The resultingoperational and performance parameters are then used in step 906 for usein various business analysis and planning purposes, described in greaterdetail herein. Information security and audibility issues associatedwith the cognitive process are then identified and addressed in step908, followed by reviews of the existing system and cognitivearchitecture, and any resulting updates, being performed in step 910.Likewise, the user experience (UX) and one or more user interfaces (UIs)associated with the cognitive process are respectively developed insteps 912 and 914.

Thereafter, solution realization operations, described in greater detailherein, are performed in step 916 to identify requirements and generatespecifications associated with data sourcing 918 and cognitive agentdevelopment 926 phases of the cognitive process lifecycle. Once solutionrealization operations are completed in step 916, data sourcing 918operations are begun in step 920 with the performance of various datadiscovery operations, described in greater detail herein. In certainembodiments, the data discovery operations may be performed by accessingvarious multi-structured, big data 304 sources, likewise described ingreater detail herein. Once the data discovery operations have beencompleted, then certain data engineering operations are performed instep 922 to prepare the sourced data for use in the cognitive agentdevelopment 926 phase. As used herein, data engineering refers toprocesses associated with data collection and analysis as well asvalidation of the sourced data. In various embodiments, the dataengineering operations may be performed on certain of themulti-structured, big data 304 sources.

Once the data sourcing 918 phase has been completed, the cognitive agentdevelopment 926 phase is begun in step 928 with development of one ormore machine learning (ML) models associated with the cognitive process.Any cognitive skills associated with the cognitive process that may notcurrently exist are composed in step 930. In certain embodiments, an MLmodel developed in step 928 may be used to compose a cognitive skill instep 930. Associated cognitive process components are then acquired instep 932 and used in step 934 to compose a cognitive agent. Theforegoing steps in the cognitive agent development 926 phase are theniteratively repeated until all needed cognitive agents have beendeveloped.

Once the cognitive agent development 926 phase has been completed,quality assurance and user acceptance operations associated with thecognitive process are respectively performed in step 936 and 938. Thecognitive process is then promoted, as described in greater detailherein, into a production phase in step 940. Once the cognitive processis operating in a production phase, ongoing system monitoring operationsare performed in step 942 to collect certain performance data. Theperformance data resulting from the monitoring operations performed instep 942 is then used in step 944 to perform various Key PerformanceIndicator (KPI) evaluation operations.

In turn, the results of the KPI evaluations are then used as feedback toimprove the performance of the cognitive process. In certainembodiments, the results of the KPI evaluations may be provided as inputin step 904 to determine additional operational and performanceparameters related to the cognitive process. In certain embodiments,these additional operational and performance parameters may be used torepeat one or more steps associated with the lifecycle of the cognitiveprocess to revise its functionality, improve its performance, or both.

FIGS. 10 a through 10 f show operations performed in a cognitive processlifecycle implemented in accordance with an embodiment of the invention.In this embodiment, a cognitive process lifecycle is begun in step 902,followed by determining certain operational and performance parametersrelated to an associated cognitive process in step 904. In certainembodiments, the operational and performance parameters determined instep 904 may include parameters related to business and technicalprocesses 1001, ideas 1002, requests 1003, needs 1104, and constraints1105, or a combination thereof.

In certain embodiments, the operational and performance parametersresulting from step 904 may then be used for various business analysisand planning purposes in step 906. In certain embodiments, the businessand planning purposes may include understanding existing business andtechnical processes 1006. In certain embodiments, the business andplanning purposes may include understanding business and technical goalsand metrics 1007. In certain embodiments, the business and planningpurposes may include analyzing business and technical pain points,return on investment (ROI), user value, technical value, and processautomation, or a combination thereof 1008.

In certain embodiments, the business and planning purposes may includeassessing business and technical fit of use cases and proposed solutions1009. In certain embodiments, the business and planning purposes mayinclude prioritizing use cases and defining Key Performance Indicators(KPIs) 1010. In certain embodiments, the business and planning purposesmay include development of a project plan 1011.

In certain embodiments, information security and audibility issuesassociated with the cognitive process may be identified and addressed instep 908. In certain embodiments, the information security andauditability issues may include defining roles and resources 1012,establishing access policies 1013, updating security policies 1014, andreviewing code for vulnerabilities 1015, or a combination thereof. Incertain embodiments, the information security and auditability issuesmay include updating log access policies 1016, establishing patch andupdate policies 1017, and updating incidence response 1018 and disasterrecovery 1019 plans, or a combination thereof.

In certain embodiments, reviews of the existing system and cognitivearchitecture, and any resulting updates, may be performed in step 910.In certain embodiments, reviews of the existing system and cognitivearchitecture, and any resulting updates, may include developing anarchitectural vision for a proposed cognitive process 1020. In certainembodiments, reviews of the existing system and cognitive architecture,and any resulting updates, may include updating certain business andcognitive process architectures 1021. In certain embodiments, reviews ofthe existing system and cognitive architecture, and any resultingupdates, may include updating certain data and technology architectures1022.

In certain embodiments, the user experience (UX) and one or more userinterfaces (UIs) associated with the cognitive process may berespectively developed in steps 912 and 914. In certain embodiments,development of the UX design may include interviewing user to understandissues 1023 associated with the cognitive process. In certainembodiments, development of the UX design may include analyzing usersand building user personas 1024 associated with the cognitive process.In certain embodiments, development of the UX design may includeestablishing user performance objectives 1025 associated with thecognitive process.

In certain embodiments, development of the UX design may includecreating user stories and scenario maps 1026 associated with thecognitive process. In certain embodiments, development of the UX designmay include the creation of one or more visual designs 1027 associatedwith the cognitive process. In certain embodiments, development of theUX design may include testing UX designs associated with the cognitiveprocess with actual users 1029. In certain embodiments, development ofthe UX design may include validating design of the UX associated withthe cognitive process with usability tests 1030.

In certain embodiments, development of the UI may include reviewing theUX design 1031 associated with the cognitive process. In certainembodiments, development of the UI may include building or assembling aUI widget library 1032 associated with the cognitive process. In certainembodiments, development of the UI may include reviewing the backendApplication Program Interface (API) associated with the cognitiveprocess. In certain embodiments, development of the UI may includedeveloping one or more UIs associated with the cognitive process.

In certain embodiments, solution realization operations may be performedin step 916 to identify requirements and generate specificationsassociated with data sourcing 918 and cognitive agent development 926phases of the cognitive process lifecycle. In certain embodiments, thesolution realization operations may include identification of datasources 1035 relevant to the cognitive process. In certain embodiments,the solution realization operations may include the creation ofspecifications for datasets 1036 required by the cognitive process. Incertain embodiments, the solution realization operations may include thedefinition of various cognitive agents 1037 associated with thecognitive process.

In certain embodiments, the solution realization operations may includethe decomposition of one or more cognitive agents into correspondingcognitive skills 1038 associated with the cognitive process. In certainembodiments, the solution realization operations may include identifyingvarious cognitive skills based upon functional requirements 1039associated with the cognitive process. In certain embodiments, thesolution realization operations may include discovery of missingcognitive skills 1040 associated with the cognitive process. In certainembodiments, the solution realization operations may include creatingspecifications for missing cognitive skills 1041 associated with thecognitive process.

In certain embodiments, the data sourcing 918 phase may be initiated instep 920 with the performance of various data discovery operations. Incertain embodiments, the data discovery operations may include variousdata exploration 1042 and data analysis 1043 operations, described ingreater detail herein. In certain embodiments, the data discoveryoperations may be performed by accessing various multi-structured, bigdata 304 sources. In certain embodiments, as described in greater detailherein, the multi-structured big data 304 sources may include publicdata 412, proprietary data 414, transaction data 416, social data 418,device data 422, ambient data 424, or a combination thereof.

In various embodiments, once the data discovery operations have beencompleted, certain data engineering operations may be performed in step922 to prepare the sourced data for use in the cognitive agentdevelopment 926 phase. In various embodiments, the data engineeringoperations may be performed on certain of the multi-structured, big data304 sources. In certain embodiments, the data engineering operations mayinclude traditional 1044 extract, transform, load (ETL) operations. Incertain embodiments, the data engineering may include cognitiveagent-assisted ETL 1045 operations. In certain embodiments, the dataengineering operations may include data pipeline configuration 146operations to skilled practitioners of the art.

In certain embodiments, once the data sourcing 918 phase has beencompleted, the cognitive agent development 926 phase may be initiated instep 928 with development of one or more machine learning (ML) modelsassociated with the cognitive process. In various embodiments,operations associated with the ML model development may includeexploratory data analysis 1047, data quality and viability assessment1048, and feature identification based upon certain datacharacteristics, or a combination thereof. In certain embodiments,operations associated with the ML model development may include featureprocessing 1050, algorithm evaluation 1051 and assessment 1052,development of new algorithms 1053, and model training 1054, or acombination thereof.

In certain embodiments, any cognitive skills associated with thecognitive process that may not currently exist may then be developed instep 930. In certain embodiments, an ML model developed in step 928 maybe used to develop a cognitive skill in step 930. In certainembodiments, operations associated with the development of a cognitiveskill may include determining the value of a particular cognitive skill1055, implementing one or more actions 1056 associated with a cognitiveskill, and deploying a cognitive skill's action 1057, or a combinationthereof. In various embodiments, operations associated with thedevelopment of a cognitive skill may include the preparation of certaintest data 1058.

In certain embodiments, operations associated with the development of acognitive skill may include defining and deploying a particularcognitive skill's metadata 1059. In certain embodiments, operationsassociated with the development of a cognitive skill may includepreparing a particular cognitive skill as a cognitive process component1060, described in greater detail herein. In certain embodiments,operations associated with the development of a cognitive skill mayinclude unit testing and debugging 1061 one or more actions associatedwith a particular cognitive skill. In certain embodiments, operationsassociated with acquiring cognitive process components may then beperformed in step 932. In certain embodiments, the operations mayinclude identifying 1062 and acquiring 1063 one or more cognitiveprocess components.

In certain embodiments, operations associated with composing a cognitiveagent may then be performed in step 934. In certain embodiments,cognitive process components acquired in step 932 may be used to composethe cognitive agent. In certain embodiments, the operations associatedwith composing a cognitive agent may include searching a repository ofcognitive process components for cognitive skills 1064 or datasets 1065associated with the cognitive process.

In certain embodiments, the operations associated with composing acognitive agent may include decomposing a cognitive agent intoassociated cognitive skills 1066. In certain embodiments, the operationsassociated with composing a cognitive agent may include composing acognitive agent 1067, establishing connections to associated cognitiveskills and data sets 1068, and deploying the cognitive agent 1069, or acombination thereof. In certain embodiments, the foregoing steps in thecognitive agent development 926 phase may then be iteratively repeateduntil all needed cognitive agents have been developed.

In certain embodiments, once the cognitive agent development 926 phasehas been completed, quality assurance and user acceptance operationsassociated with the cognitive process are respectively performed in step936 and 938. In certain embodiments, the quality assurance operationsmay include establishing test plans 1070 for the cognitive process. Incertain embodiments, the quality assurance operations may includeverifying the cognitive process meets specified requirements 1071associated with the cognitive process.

In certain embodiments, the quality assurance operations may includevalidating the cognitive process fulfill its intended purpose 1072. Incertain embodiments, the quality assurance operations may includeassessing the cognitive process' rate of learning 1073. In certainembodiments, the use acceptance operations may include validating thecognitive process fulfills its intended purpose 1074. In certainembodiments, the user acceptance operations may include assessing thecognitive process in the context of the user's organization 1073.

In certain embodiments, the cognitive process is then promoted in step940, as described in greater detail herein, into a production phase. Incertain embodiments, operations associated with the production phase mayinclude deploying one or more cognitive agents into production 1076. Incertain embodiments, operations associated with the production phase mayinclude capturing and reprocessing data generated by the system 1077. Incertain embodiments, operations associated with the production phase mayinclude monitoring the system's technical performance 1078.

In certain embodiments, once the cognitive process is operating in theproduction phase, ongoing system monitoring operations are performed instep 942 to collect certain performance data. In certain embodiments,the system monitoring operations may include updating a continuousintegration process 1079. In certain embodiments, the system monitoringoperations may include updating infrastructure monitoring processes1080.

In certain embodiments, the performance data resulting from themonitoring operations performed in step 942 may them be used in step 944to perform various Key Performance Indicator (KPI) evaluationoperations. In certain embodiments, the KPI evaluation operations mayinclude monitoring 1081 and analyzing 1082 the system's businessperformance. In certain embodiments, the KPI evaluation operations mayinclude making recommendations to improve 1083 the systems businessperformance.

In certain embodiments, the results of the KPI evaluations may be usedas feedback to improve the performance of the cognitive process in theproduction 940 phase. In certain embodiments, the results of the KPIevaluations may be provided as input in step 904 to determine additionaloperational and performance parameters related to the cognitive process.In certain embodiments, these additional operational and performanceparameters may be used to repeat one or more steps associated with thelifecycle of the cognitive process to revise its functionality, improveits performance, or both.

FIGS. 11 a and 11 b are a simplified process flow showing the lifecycleof augmented intelligence agents implemented in accordance with anembodiment of the invention to perform augmented intelligence system(AIS) operations. In certain embodiments, a cognitive agent lifecyclemay include a cognitive agent composition 1102 phase and a cognitiveagent confirmation 1104 phase. In certain embodiments, the cognitiveagent composition 1102 phase may be initiated with the definition of acognitive process use case in step 1106, followed by architecting anassociated solution in step 1108.

One or more cognitive skills associated with the architected solutionare then defined, developed and tested in step 1110. In certainembodiments, a machine learning (ML) model associated with thearchitected solution is defined in step 1112. In certain embodiment,cognitive actions, described in greater detail herein, are defined forthe ML model in step 1114. In certain embodiments, data sources for thearchitected solution are identified in step 1116 and correspondingdatasets are defined in step 1118.

The ML model definitions defined in step 1112 are then used in step 1120to define variables that need to be secured in the implementation ofeach associated AIS region, described in greater detail herein.Likewise, the data sources identified in step 1116 are used in step 1122to define data sources corresponding to each associated AIS region.Thereafter, the data sources defined in step 1122 and the datasetsdefined in step 1118 are used in step 1124 to define datasets that willbe used to compose a cognitive agent in step 1128. Once the datasetshave been developed in step 1124, they are used to curate and uploadtraining data to associated data source connections in step 1126.

Cognitive agent compositions operations are then initiated in step 1128by creating a cognitive agent instance in step 1130. Once created, thesecured variables defined in step 1120 are added to one or morecognitive skills, which in turn are configured in step 1132. The MLmodel actions defined in step 914 are then used in step 1134 to defineinput and output services for the one or more cognitive skillsconfigured in step 1132. Thereafter, the datasets developed in step 1124are used in step 1136, along with the training data curated and uploadedin step 1126 to define dataset connections. A dataflow is then createdfor the cognitive agent in step 1138 and mapped in step 1140.

The cognitive agent confirmation 1104 phase is then initiated in step1142 by testing various service associated with the cognitive agentcomposed in step 1128. Thereafter, a cognitive agent snapshot 1144,described in greater detail herein, is then created in step 1144. Incertain embodiments, the cognitive agent snapshot 1144 may includeversioning and other descriptive information associated with thecognitive agent.

An instance of the cognitive agent is then initiated in step 1146. Incertain embodiments, initiation of the cognitive agent may includepromoting a snapshot of the cognitive agent in step 1148 and enablingstart and stop triggers in step 1150. The instance of the cognitiveagent that was initiated in step 1146 is then invoked for execution instep 1152, followed by performing queries and filtering associatedresponses in step 1154. In certain embodiments, log entriescorresponding to the operations performed in step 1142 through 1154 arereviewed in step 1156.

FIG. 12 is a simplified block diagram of an augmented intelligencesystem (AIS) implemented in accordance with an embodiment of theinvention to perform pattern-based continuous learning operations. Incertain embodiments, the pattern-based continuous learning operations1202 may include a data ingestion and processing 1204 phase, followed bythe performance of certain cognitive processes, described in greaterdetail, during a cognitive insights 1206 phase. In certain embodiments,the cognitive insight 1206 phase is followed by a cognitive action 1208phase, which in turn is followed by a cognitive learning 1210 phase. Incertain embodiments, the process is continued, proceeding with the dataingestion and processing 1204 phase.

In certain embodiments, multi-structured big data sources 304 may bedynamically ingested during the data ingestion and processing 1204phase. In certain embodiments, based upon a particular context,extraction, parsing, and tagging operations are performed on language,text and images they contain to generate associated datasets 1214. Incertain embodiments, the resulting datasets may include varioustransaction histories 1216, customer relationship management (CRM) feeds1218, market data feeds 1220, news feeds 1222, social media feeds 1224,and so forth.

In certain embodiments automated feature extraction and modelingoperations may be performed on the datasets 1214 to generate one or morecognitive models 222, described in greater detail herein. In certainembodiments, the cognitive models 222 may include quantitative 1228models, qualitative 1230 models, ranking 1232 models, news topic 1234models, sentiment 1236 models, and so forth. In various embodiments, theresulting cognitive models may be implemented to map certain datasets1214 to a cognitive graph 260, described in greater detail herein.

In various embodiments, the cognitive models 222 and the datasets 1214mapped to the cognitive graph 260 may be used in the composition ofcertain cognitive skills 226, as likewise described in greater detailherein. In certain embodiments, the cognitive skills 226 may include aportfolio profile builder 1242 skill, a client profile builder 1244skill, a market data pipeline 1246 skill, a market event detection 1248skill, a cognitive insights ranking 1250 skill, and so forth. Aslikewise described in greater detail herein, the resulting cognitiveskills 226 may then be used in various embodiments to generate certaincognitive agents 250. In certain embodiments, the resulting cognitiveagents 250 may include sourcing 432 agents, destination 434 agents,engagement 436 agents, compliance 438 agents, and so forth.

In certain embodiments, the sourcing 432 agent may be implemented tosource various multi-structured big data 304 sources, described ingreater detail herein. In certain embodiments, the sourcing 432 agentmay include a batch upload agent, an Application Program Interface (API)connectors agent, a real-time streams agent, a Structured Query Language(SQL)/Not Only SQL (NoSQL) databases agent, a message engines agent, atransaction sourcing agent, or some combination thereof. Skilledpractitioners of the art will recognize that many such examples ofsourcing 432 agents are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention.

In certain embodiments, the resulting cognitive agents 250 may beimplemented during the cognitive insights 1206 phase, as described ingreater detail herein, to perform various cognitive processes 326. Invarious embodiments, the performance of certain cognitive processes 326may result in the generation of one or more cognitive insights. Invarious embodiments, the cognitive insights may include the provision ofcertain actionable recommendations and notifications to a user duringthe cognitive action 1208 phase. In various embodiments, certainfeatures from newly-observed data may be automatically extracted fromuser feedback during the learning 1010 phase to improve variouscognitive models 222.

As an example, a first query from a user may be submitted to the AIS 118system during the cognitive insights 1206 phase, which results in thegeneration of a first cognitive insight, which is then provided to theuser. In response, the user may respond by providing a first response,or perhaps a second query, either of which is provided in the samecontext as the first query. The AIS 118 receives the first response orsecond query, performs various AIS 118 operations, and provides the usera second cognitive insight. As before, the user may respond with asecond response or a third query, again in the context of the firstquery. Once again, the AIS 118 performs various AIS 118 operations andprovides the user a third cognitive insight, and so forth. In thisexample, the provision of cognitive insights to the user, and theirvarious associated responses, results in a stateful dialog that evolvesover time.

FIG. 13 is a simplified block diagram of components associated with theoperation of an augmented intelligence system (AIS) governance andassurance framework implemented in accordance with an embodiment of theinvention to provide AIS assurance. Certain embodiments of the inventionreflect an appreciation that the practices and processes associated withAI assurance, described in greater detail herein, ideally provide aneffective framework for the management of AI bias, robustness, andexplainability. AI bias, as used herein, broadly refers to an outcome ofa cognitive computing operation deviating from a standard resulting indisproportionate favor of or against one concept, thing, action,decision, person, or group compared with another, usually in a wayconsidered to be prejudicial, discriminatory or inequitable.

Certain embodiments of the invention likewise reflect an appreciationthat such deviation may take many forms. For example, the deviation maytake the form of statistical bias, in which an estimate deviates from astatistical standard or a true population value. As another example, thedeviation may take the form of a judgement, decision, or action thatdiverges from a moral norm. As yet another example, the deviation maytake the form of disregard or avoidance of regulatory or legal statutes.As yet still another example, the deviation may take the form ofcultural, racial, gender, physiological, psychological, geographical,and socioeconomic prejudices. Those of skill in the art will recognizethat many types of bias and associated deviation are possible.Accordingly, the foregoing is not intended to limit the spirit, scope orintent of the invention.

Certain embodiments of the invention reflect an appreciation that theoccurrence of AI bias may result in a disparate impact upon the outcomeof an associated cognitive computing operation. Certain embodiments ofthe invention likewise reflect an appreciation that AI bias may not beintentional. Likewise, certain embodiments of the invention reflect anappreciation that AI bias may be inherent in data used to train machinelearning models. Furthermore, certain embodiments of the inventionreflect an appreciation that the determination of whether the outcome ofa particular cognitive computing operation constitutes AI bias may besubjective. Accordingly, such determination, and the degree to which aparticular outcome embodies AI bias, is a matter of design choice.

As used herein, machine learning (ML) broadly refers to a class oflearning algorithms that include artificial neural networks, decisiontrees, support vector machines, and so forth. Skilled practitioners ofthe art will be aware that such algorithms are able to learn fromexamples and can typically improve their performance by processing moredata over time. Those of skill in the art will likewise be aware thatthe data used to train a learning algorithm may include a variety ofunstructured data forms including free-form text, spoken language,images, and so forth.

As likewise used herein, an ML model is a mathematical representation ofa real-world process that can be facilitated by a cognitive computingoperation. Skilled practitioners of the art will likewise be aware an MLmodel is typically generated by providing certain training data to oneor more learning algorithms associated with the model. In turn, thelearning algorithm finds patterns in the training data such that certaininput parameters correspond to a particular target. The output of thetraining process is an ML model which can then be used to makepredictions. In certain embodiments, the training data may provide thebasis for the learning algorithm to provide recommendations, performmedical diagnoses, make investment decisions, allow autonomous vehiclesto recognize stop signs, and so forth.

Certain embodiments of the invention reflect an appreciation thatmachine learning is a statistical approach to AI, and as such, may bedifficult to interpret and validate. Furthermore, certain embodimentslikewise reflect an appreciation that automated learning operations thatuse inherently biased data will likely lead to biased results.Accordingly, various embodiments of the invention reflect anappreciation that AI bias may unintentionally be inherent in the designof certain ML models used to perform a cognitive computing operation.

Likewise, certain embodiments of the invention reflect an appreciationthat artificial agents used in the performance of a cognitive computingoperation, described in greater detail herein, may impose systematicdisadvantages on subgroups based upon patterns learned via proceduresthat appear reasonable and nondiscriminatory on face value. Furthermore,certain embodiments of the invention reflect an appreciation thatartificial agents may paradoxically learn autonomously fromhuman-derived data, which may in turn result in inadvertently learnedhuman biases, whether good or bad. Moreover, certain embodiments of theinvention reflect an appreciation that while autonomous systems, such asan AIS 118, might be regarded as neutral or impartial, they maynonetheless employ biased algorithms that result in significant harmthat could go unnoticed and uncorrected, possibly until it is too late.

As likewise used herein, and as it relates to AI assurance, AIrobustness broadly refers to the ability of an ML model to withstand,and overcome, perturbations that may have an adverse effect on itsintended operation. Certain embodiments of the invention reflect anappreciation that there is an inherent level of risk, unpredictability,and volatility in real-world settings where AI systems, such as an AIS118, operate. Accordingly, certain embodiments of the invention likewisereflect an appreciation that ML models typically used by such systemsneed to be resilient to unforeseen events and adversarial attacks thatcan result in damage or manipulation. Likewise, certain embodiments ofthe invention reflect an appreciation that various approaches toachieving AI robustness may include avoiding known risks,self-stabilization, and graceful degradation.

Certain embodiments of the invention likewise reflect an appreciationthat examples of challenges to AI robustness include distributionalshift, adversarial inputs, and unsafe exploration. Likewise, certainembodiments of the invention reflect an appreciation that ML models areprone to various attacks and threats. For example, deep learning modelsare known to have performed well when performing image recognitiontasks. However, it is also known that such models are prone toadversarial attacks. To continue the example, two images may lookessentially the same to a human, but when presented to a model, they mayproduce different outcomes. In this example, the two images are inputdata points that may vary slightly, and as such, represent anadversarial attack. Accordingly, while the differences in the two imagesmay seem indistinguishable to a human, they may be different enough toan ML model to result in different outcomes.

Likewise, AI explainability, as used herein and as it relates to AIassurance, broadly refers to transparently conveying to a user thestructural and operational details of the ML model(s) used by an AIS,statistical and other descriptive properties of its associated trainingdata, and various evaluation metrics from which its likely behavior maybe inferred. Certain embodiments of the invention reflect anappreciation that as AI approaches become more sophisticated, decisionsare increasingly being made by ML models whose design, and the rationaleof its decision making processes, are opaque to the user. Certainembodiments of the invention likewise reflect an appreciation that theopaqueness of such ML models hinders AI explainability, and byextension, undermines a user's trust of the outcomes it produces.Accordingly, certain embodiments of the invention reflect anappreciation that AI explainability ideally provides a userinterpretable insight into how and why an ML model performed certainactions or arrived at a particular decision.

Certain embodiments of the invention reflect an appreciation that manyAI applications use ML models that essentially operate as black boxes,offering little if any discernible insight into how they reach theiroutcomes. Certain embodiments of the invention likewise reflect anappreciation that such opaque operation may be suitable for modest orfairly inconsequential decisions, such as recommending apparel to wearor a movie to view. However, certain embodiments of the inventionlikewise reflect an appreciation that a user's trust in an opaque MLmodel begins to diminish when the decision is related to something morecomplex or important, such as recommendations for healthcare orfinancial investments. As an example, how many users would trust anopaque ML model's diagnosis rather than a physician's without somedegree of clarity regarding how the model arrived at its recommendation?In this example, the model's diagnosis may in fact be more accurate.

However, lack of explainability may lead to a lack of trust.Accordingly, certain embodiments of the invention reflect anappreciation that AI explainability can assist in making a black box MLmodel's decision making process less opaque in a way that iscomprehensible to humans. As used herein, as it relates to a black boxML model's decision making process, less opaque broadly refers toproviding sufficient visibility into the method by which a particulardecision was made, the factors contributing to the decision, and theirrespective effect on the decision, such that a user can understand howand why the decision was made. Certain embodiments of the inventionreflect an appreciation that the extent of, or degree of detail, suchvisibility may need to be provided may vary according to the particularneeds of the user, the complexity of the decision, the context of thedecision, or a combination thereof. Accordingly, the extent of suchvisibility, and the method by which it is generated and provided, is amatter of design choice.

Referring now to FIG. 13 , an AIS 118 may be implemented in certainembodiments to include an AIS governance and assurance framework 128. Incertain embodiments, the AIS governance and assurance framework 128 mayin turn be implemented to include an AIS assurance engine 1330, anopaque model 1332, and an output module 1350. In various embodiments,the AIS assurance engine 1330 may be implemented to include acounterfactual engine 1336, and certain cognitive applications 326. Incertain embodiments, the opaque model 1332 may be variously implementedas an opaque ML model, an opaque cognitive model, an opaque classifier,black box ML model, a black box classifier, and so forth. Skilledpractitioners of the art will recognize that many such embodiments of anopaque model 1332 are possible. Accordingly, the foregoing is notintended to limit the spirit, scope or intent of the invention. Incertain embodiments, the counterfactual engine 1336 may be implementedas a Counterfactual Explanations for Robustness, Transparency,Interpretability, and Fairness of Artificial Intelligence (CERTIFAI)tool.

In certain embodiments, the AIS assurance engine 1330 may be implementedto perform an AIS assurance operation. In certain embodiments, the AISassurance operation may include the performance of an AIS impartialityassessment operation, an AIS robustness assessment operation, an AISexplainability operation, an AIS explainability with recourse operation,or a combination thereof, as described in greater detail herein. Incertain embodiments, the AIS assurance operation may be performed on aservice provider server, described in greater detail herein. In certainembodiments, performance of the AIS assurance operation may be providedas an AIS assurance service.

In certain embodiments, the AIS assurance service may be referred to asAIS Trust as a Service. Certain embodiments of the invention reflect anappreciation that trust, as it relates to a an AIS 118 used to generatea particular decision 1334, may be subjective. However, certainembodiments of the invention likewise reflect an appreciation that theperformance of an AIS assurance operation, whether provided as AIS trustas a service or in some other form, may contribute to establishing, andreinforcing, a user's 1306 trust in the decisions 1334 generated by anopaque model 1332.

In various embodiments, the AIS assurance engine 1330 may be implementedas a foundation layer, described in greater detail herein, for certaincognitive applications 326. In certain embodiments, the cognitiveapplications 326 may be implemented to include an AIS impartialityassessment 1342 engine, an AIS robustness assessment 1344 engine, and anAIS explainability generation 1346 engine, or a combination thereof. Incertain embodiments, the AIS impartiality assessment 1342 engine may beimplemented to perform an AIS impartiality assessment operation. Incertain embodiments, the AIS impartiality assessment operation may beperformed to detect the presence of bias in a particular ML model, suchas the opaque model 1332, and if detected, assess its effect on theoutcome of an associated cognitive computing operation.

In certain embodiments, the AIS robustness assessment 1344 module may beimplemented to perform an AIS robustness assessment operation. Incertain embodiments, the AIS robustness assessment operation may beperformed to assess the robustness of a particular ML model, such as theopaque model 1332. In certain embodiments, the AIS explainabilitygeneration 1346 module may be implemented to perform an AISexplainability operation. In certain embodiments, the AIS explainabilityoperation may be performed to provide a user interpretable insight intohow and why a particular ML model, such as the opaque model 1332,performed certain actions or arrived at a particular decision 1334. Incertain embodiments, the decision 1334 may be implemented as aclassification, a determination, a conclusion, a prediction, an outcome,or a combination thereof.

In certain embodiments, a training corpus 1302, familiar to those ofskill in the art, may be used by a model trainer 1304, likewise familiarto skilled practitioners of the art, to train the opaque model 1332. Incertain embodiments, the training corpus 1302 may include one or moredatasets pertinent to the training of the opaque model 1332. In certainembodiments, the model trainer 1304 may be implemented to perform aclassifying operation. In certain embodiments, performing theclassifying operation results in certain data elements included in thetraining corpus 1302 being trained for use by the opaque model 1332. Incertain embodiments, an opaque model 1332 developer may select, orprovide, a particular training corpus 1302 and a particular modeltrainer 1304 to train the opaque model 1332. In these embodiments, theselection of which training corpus 1302 and model trainer 1304 are usedto train the opaque model 1332 is a matter of design choice.

In certain embodiments, a data point obtainer 1310 may be implemented toobtain one or more input data points 1308 associated with a particularuser 1306, a group of users 1306, or other entity. As used herein, aninput data point 1308 broadly refers to any discrete unit of informationthat may be used by an opaque model 1332 to produce a decision 1334. Incertain embodiments, the data point obtainer 1310 may likewise beimplemented to provide one or more obtained input data points 1308 tothe opaque model 1332 for processing. In certain embodiments, one ormore decisions 1334 may be generated by the opaque model 1332 accordingto the one or more input data points 1308.

As described in greater detail herein, certain aspects of the inventionreflect an appreciation that the adoption of ML models, includingvarious implementations of an opaque model 1332, is currently increasingat an unprecedented pace. Certain aspects of the invention likewisereflect an appreciation that such adoption has led to a variety ofconsiderations related to potential ethical, moral, and socialconsequences of the decisions 1334 made by such models. For example, onesuch consideration may be related to being able to determine whether theopaque model 1332 been partial to, or biased against, a particular user1306, group of users 1306, or other entity.

Another consideration may be related to being able to determine howeasily an opaque model 1332 might be deceived, broken, or otherwisecompromised. Yet another consideration may be related to how a user 1306of such models, or their developer, might be able to understand how aparticular opaque model 1332 makes its decisions 1334. Yet still anotherconsideration may be related to what a particular user 1306, group ofusers 1306, or other entity, might be able to do to change anunfavorable outcome resulting from a decision 1334 made by an opaquemodel 1332.

To provide an example of how such considerations may be applicable tosupporting AIS assurance for a particular decision 1334 generated by anopaque model 1332, a user 1306 may have provided certain input datapoints 1308 in the course of applying for a loan. In this example, theinput data points 1308 provided to the opaque model 1332 may haveresulted in a decision 1334 to decline the user's 1306 loan application.However, the decision making process of the opaque model 1332 is notvisible to the user 1306. As a result, the user 1306 may have no way ofunderstanding why their loan application was declined without theprovision of corresponding AIS explainability.

In certain embodiments, the counterfactual engine 1336 may beimplemented to perform a counterfactual generation operation. In certainembodiments, the counterfactual generation operation may be performed togenerate one or more counterfactuals. As used herein, a counterfactualbroadly refers to another data point that is close to a particular inputdata point 1308 whose use would result in an ML model, such as theopaque model 1332, producing a different outcome corresponding to theinput data point 1308. In certain embodiments, the generation of one ormore such counterfactuals by the counterfactual engine 1336 maycontribute to the provision of AIS explainability with recourse to oneor more decisions 1334, assessing the AIS robustness of the opaque model1332, the extent of any bias it may embody, or a combination thereof.

In certain embodiments, the counterfactual engine 1336 may beimplemented to generate one or more counterfactuals, which can then beused by the AIS explainability generation 1346 engine to perform an AISexplainability with recourse operation. As used herein, AISexplainability with recourse broadly refers to the provision of AISexplainability to a user 1306, in combination with one or morecounterfactuals that the user 1306 may employ as a recourse to changinga particular decision 1334 made by the opaque model 1332. In certainembodiments, the explainability with recourse operation may be performedby the AIS explainability generation 1346 engine as one aspect of an AISassurance operation.

In certain embodiments, the explainability with recourse operation maybe performed to provide an AIS assurance explanation to a user 1306. Asused herein, an AIS assurance explanation broadly refers to anexplanation of how a particular decision 1334 was made by the opaquemodel 1332, the factors contributing to the decision 1334, and theirrespective effect on the decision 1334, such that a user 1306 can beassured of the validity of the decision 1334. In certain embodiments,the AIS assurance explanation provided to the user 1306 may include oneor more counterfactuals that may change a particular decision 1334 madeby the opaque model 1332. In certain embodiments, a counterfactual maybe provided to the user 1306 in the form of a recourse. In certainembodiments, the AIS assurance explanation may be implemented to containone or more assertions related to one or more counterfactuals that maychange a particular decision 1334 made by the opaque model 1332. Incertain embodiments, the AIS assurance explanation may be provided tothe user 1306 in the form of a cognitive insight 1352.

In certain embodiments the AIS assurance explanation provided to theuser 1306 may include two or more AIS assurance explanations so they canchoose which changes might be made to achieve a desired outcome. Forexample, as previously described in greater detail herein, the opaquemodel 1332 may have processed certain input data points 1308 submittedby a user 1306 in the course of applying for a loan. As likewisedescribed in greater detail herein, the loan application submitted mayhave been processed by the opaque model 1332, resulting in theapplication being declined.

To further extend the previous example, the AIS assurance explanationmay be, “Had your income been $5,000.00 greater per year, or if yourcredit score had been 30 points higher, your loan would have beenapproved.” Accordingly, such counterfactuals may be implemented invarious embodiments to not only provide a way of explaining decisions1334 made by an opaque model 1332 to a user 1306, but also recoursesthat may be used to identify actionable ways of changing certainbehaviors or other factors to obtain favorable outcomes. In certainembodiments, such counterfactuals may be implemented to audit theimpartiality and robustness of an opaque model 1332.

In certain embodiments, the counterfactual engine 1336 may beimplemented to use a genetic algorithm to generate one or morecounterfactuals. As used herein, a genetic algorithm broadly refers to amathematical approach to solving both constrained and unconstrainedoptimization problems, based upon natural selection, the process thatdrives biological evolution. In typical implementations, a geneticalgorithm repeatedly modifies a population of individual solutions. Ateach step, the genetic algorithm selects individuals at random from thecurrent population to be parents and uses them to produce the childrenfor the next generation. Over successive generations, the populationevolves toward an optimal solution. In certain embodiments, a customizedgenetic algorithm may be implemented to iteratively improve the set ofgenerated data points such that they become closer to a particular inputdata point 1308 while simultaneously ensuring the opaque model 1332gives decisions 1334 for the generated data points that differ from adecision 1334 corresponding to the input data point 1308.

Skilled practitioners of the art will be aware that genetic algorithmscan be applied to solve a variety of optimization problems that are notwell suited for standard optimization algorithms, including problems inwhich the objective function is discontinuous, non-differentiable,stochastic, or highly nonlinear. Likewise, genetic algorithms can beimplemented to address problems of mixed integer programming, where somecomponents are restricted to be integer-valued. As typicallyimplemented, a genetic algorithm uses three types of rules at each stepof selection to create the next generation from the current population.First, selection rules are used to select certain individuals, referredto as parents, that contribute to the population at the next generation.Second, crossover rules combine two parents to form children for thenext generation, and third, mutation rules apply random changes toindividual parents to form children.

Certain embodiments of the invention reflect an appreciation that theuse of a genetic algorithm to allows the generation of counterfactualsfor both linear and nonlinear models (e.g. deep networks), and for anyform of input data, from mixed tabular data to image data, without anyapproximations to, or assumptions for, the opaque model 1332. In certainembodiments, a user 1306 may both define a range for any particularfeature and restrict which features can change. As used herein, afeature broadly refers to an individual measurable property orcharacteristic of a phenomenon being observed. In certain embodiments,the counterfactual engine 1336 may be implemented to constrain thevalues of sampled points based upon those choices, allowing thegenerated counterfactuals to reflect a user's 1306 understanding of howmuch it is possible to change their associated features.

Certain embodiments of the invention reflect an appreciation thatcareful selection of informative, discriminating, and independentfeatures may contribute to the efficacy of algorithms used in patternrecognition, classification, and regression. Certain embodiments of theinvention likewise reflect an appreciation that while features are oftennumeric, structural features such as strings and graphs are often usedin syntactic pattern recognition. Likewise, certain embodiments of theinvention reflect an appreciation that the concept of such features isrelated to the concept of an explanatory variable used in statisticaltechniques such as linear regression. In certain embodiments, the inputdata point 1308 may be implemented to include multiple features.

In certain embodiments, the data point obtainer 1310 may be implementedto provide a particular input data point 1308 x to the opaque model1332, where it is used by the counterfactual engine 1336 f to generate afeasible counterfactual c, as follows:

$\begin{matrix}{{\min\limits_{c}\mspace{14mu}{d\left( {x,c} \right)}}{{s.t.\mspace{14mu}{f(c)}} \neq {f(x)}}} & (1)\end{matrix}$

where d(x, c) is the distance between x and c.

In certain embodiments, the counterfactual engine 1336 may beimplemented to avoid using approximations to, or assumptions for, theopaque model 1332 by using a customized genetic algorithm to solve theprior equation. In these embodiments, the customized genetic algorithmmay be implemented to work for any black box model, such as the opaquemodel 1332, and input data types, such as input data point 1308 x.Accordingly, in certain embodiments, the customized genetic algorithmmay be implemented to be model-agnostic. Likewise, a certain degree offlexibility in the generation of counterfactuals may be provided invarious embodiments of the invention through the use of the customizedgenetic algorithm.

In certain embodiments, the counterfactual engine may be implemented tosolve the optimization problem posed by equation (1) through the processof natural selection, as described in greater detail herein. In certainembodiments, the only mandatory inputs for the customized geneticalgorithm are the counterfactual engine 1336 f and a particular inputdata point 1308 x. In general, for an n-dimensional input vector x, letW∈□^(n) represent the space from which individuals can be generated andP be the set of points with the same prediction as x, as follows:P={p|f(P)=f(x),p∈W}  (2)

where the possible set of individuals c∈I are defined such that:I=W\P  (3)

with each individual c∈I being a candidate counterfactual.

Certain embodiments of the invention reflect an appreciation that thegoal of this approach is to find the fittest possible c* to xconstrained on c*∈I. Accordingly, the fitness for an individual c isdefined as:

$\begin{matrix}{{fitness} = \frac{1}{d\left( {x,c} \right)}} & (4)\end{matrix}$

Here c*will then be the point closest to x such that c*∈I. For amulti-class case, if a user wants the counterfactual c to belong to aparticular class j, we define Q as:Q={q|f(q)=j,q∈W}  (5)

Accordingly, equation (3) then becomes:I=(W\P)ΩQ  (6)

In certain embodiments, the customized genetic algorithm is carried outas follows: first, a set I_(c) is built by randomly generating pointssuch that they belong to I. Individuals c∈I_(c) are then evolved throughthree rules processes: selection, mutation, and crossovers, as describedin greater detail herein. Accordingly, the selection rules processchooses individuals that have the best fitness scores resulting fromequation (4). A proportion of these individuals, dependent upon p_(m),the probability of mutation, are then subjected to mutation, whichinvolves arbitrarily changing some feature values. A proportion ofindividuals, dependent on p_(c), the probability of crossover, are thensubjected to crossover, which involves randomly interchanging somefeature values between individuals. The resulting population is thenrestricted to the individuals that meet the required constraint fromequation (3) or equation (6), and the fitness scores of the newindividuals are calculated. This process is repeated until the maximumnumber of generations is reached. Finally, the individual(s) c* with thebest fitness scores are chosen as the desired counterfactuals.

In certain embodiments, the choice of distance function used in equation(1) may depend upon details provided by the opaque model 1332 creatorand the type of data being considered. For example, if the data istabular, the L₁ norm normalized by the median absolute deviation (MAD)is better than using the L₁ or L₂ norm for counterfactual generation.Accordingly, in certain embodiments the L₁ norm for continuous features(NormAbs) and a simple matching distance for categorical features(SimpMat) are chosen as a default for tabular data,

In certain embodiments, using MAD for normalization in model developmentis not possible when training data, such as a training corpus 1302, isunavailable. However, when access to training data is available,normalization is possible, with the distance metric determined asfollows:

$\begin{matrix}{{d\left( {x,c} \right)} = {{\frac{n_{con}}{n}{{NormAbs}\left( {x,c} \right)}} + {\frac{n_{cat}}{n}{{SimpMat}\left( {x,c} \right)}}}} & (7)\end{matrix}$

-   -   where n_(con) and n_(cat) are the number of continuous and        categorical features, respectively, and n is the total number of        features (n_(con)+n_(cat)=n).

Certain embodiments of the invention reflect an appreciation that forimage data, the Euclidean distance and absolute distance between twoimages are not good measures of image similarity. Accordingly,Structural Similarity Index Measure (SSIM) may be used in certainembodiments for image data, as it generally provides a better measure ofwhat humans consider to be similar images. As typically implemented,SSIM values lie between 0 and 1, where a higher SSIM value means thattwo images look more similar to each other. Accordingly, for input datapoint 1308 image x and counterfactual image c, the distance can bedetermined as follows:

$\begin{matrix}{{d\left( {x,c} \right)} = \frac{1}{{SSIM}_{({x,c})}}} & (8)\end{matrix}$

In certain embodiments, the outcomes produced by the customized geneticalgorithm used by the opaque model 1332 may be improved through the useof additional inputs beyond a particular input data point 1308.

In certain embodiments, auxiliary constraints may be incorporated toensure feasible solutions by restricting the space defined by the set W,which represents the space from which individuals can be generated. Asan example, for an n-dimensional input, let W be the Cartesian productof the sets W₁, W₂ . . . W_(n). As another example, for continuousfeatures, W_(i) can be constrained as W_(i)∈[W_(imin), W_(imax)], andcategorical features can be constrained as W_(i)∈{W₁, W₂ . . . W_(j)}.However, in various embodiments certain variables might be immutable(e.g., a person's race). In these embodiments, a feature i for an inputx can be muted by setting Wi=xi.

As an example, a user 1306 whose loan application was declined may beprovided an AIS assurance explanation, described in greater detailherein, that the loan was not approved due to insufficient income. Inthis example, a counterfactual may have been generated by thecounterfactual engine 1336, with a suggested recourse stating the loanmay have been granted if the user's 1306 income was increased from$10,000 a year to $900,000. To continue the example, such an increasemay not be feasible for the user 1306, and as a result, employing thecounterfactual is not a practical option for the user 1306. Accordingly,an appropriate constraint might be applied, such as W_(i)∈[$10,000,$15,000] to constrain the increase in income to an amount that may beachievable. Likewise, the number of counterfactuals k can also be set.To continue the example further, the counterfactual engine 1332 may beconfigured to choose the top k individuals (with k=1 as default), wheredifferent features have changed, such that the user 1306 can be providedmultiple and diverse explanations.

In certain embodiments, the AIS robustness assessment 1344 engine may beconfigured to receive the one or more counterfactuals from thecounterfactual engine 1336. In certain embodiments, the AIS robustnessassessment 1344 engine may be implemented to determine distances betweenan input data point 1308 and a plurality of proximate counterfactuals,as described in greater detail herein. As used herein, as it relates tothe distance separating an input data point 1308 and a particularcounterfactual, proximate broadly refers to those counterfactuals thatare nearest to the input data point 1308 (i.e., have the shortestrelative distance vectors), as described in greater detail herein. Incertain embodiments, the AIS robustness assessment 1344 engine may beconfigured to use such distances to determine the robustness of a targetopaque model 1332 based upon a statistical operation performed on thedetermined distances of the plurality of proximate counterfactuals. Forexample, a statistic may be a mean of the distances.

Certain embodiments of the invention reflect an appreciation that themaximum distance used to determine whether a particular counterfactualis proximate to the input data point may be subjective. Certainembodiments of the invention likewise reflect an appreciation that themaximum distance selected to determine whether a particularcounterfactual is proximate to the input data point may be used todetermine which, and how many, counterfactuals are proximate.Accordingly, the maximum distance used in these embodiments to determinewhether a particular counterfactual is proximate to the input datapoint, and the method by which it is selected, is a matter of designchoice.

Certain embodiments of the invention reflect an appreciation that giventwo black-box models, such as the opaque model 1332, one network wouldbe more difficult to deceive if the counterfactuals across classes, onaverage, are farther away from the input instances, such as an inputdata point 1308, compared to the other network. In certain embodiments,the counterfactual engine 1336 may be implemented to provide a measureof distance d(x,c), which can be used in to generate a CounterfactualExplanation-based Robustness Score (CERScore), for a particular opaquemodel 1332.

As used herein, CERScore is defined herein as the expected distancebetween the input instances (e.g., input data point 1304) and theircorresponding counterfactuals, such that:

$\begin{matrix}{{{CERScore}({model})} = {\,_{X}^{\mathbb{E}}\left\lbrack {d\left( {x,c^{*}} \right)} \right\rbrack}} & (9)\end{matrix}$

-   -   where a higher CERScore implies that the associated opaque model        1332 is more robust.        In certain embodiments, the counterfactual engine 1336 may be        implemented to provide the CERScore solely through the use of        the opaque cognitive model's 1332 predictions, or decisions        1334, without a priori knowledge of its internal structure or        operation.

In certain embodiments, the AIS robustness assessment 1344 engine may beimplemented to perform an AIS robustness assessment operation. Incertain embodiments, performance of the robustness assessment operationmay include the generation of a CERScore for a particular opaque model1332. In certain embodiments, the AIS robustness assessment operationmay be performed to provide an AIS assurance explanation to a user 1306.In certain embodiments, the AIS assurance explanation provided to a user1306 may be implemented to include a CERScore for a particular opaquemodel 1332. In certain embodiments, the AIS assurance explanation may beprovided to the user 1306 in the form of a cognitive insight 1352.

In certain embodiments, the cognitive insight 1352 provided to a usermay be in the form of an electronic message, on-screen display, printedpage, or the like. In certain embodiments, the output module 1350 may beimplemented to provide the cognitive insight 1352 to a particular user1306. In certain embodiments, the AIS assurance explanation may bepresented to the developer of the model, such that the developer canmodify the model, such as the opaque model 1332, the training corpus1302, the model trainer 1304, or a combination thereof. Those of skillin the art will recognize that the described presentation of the AISassurance explanation as an cognitive insight 1352 provides thedeveloper of the opaque model 1332 a basis for modifying the model, thetraining corpus 1302, the model trainer 1304, or a combination thereof,to achieve more robust results.

In certain embodiments, the AIS impartiality assessment 1342 engine maybe configured to receive the one or more counterfactuals from thecounterfactual engine 1336. In certain embodiments, the AIS impartialityassessment 1342 engine may be implemented to contrast features between asubject data point (e.g., input data point 1304) and the receivedcounterfactuals and identify significant contrasts between them. As usedherein, significant contrast broadly refers to a noteworthy differencein the respective value of a particular feature shared by a subject datapoint and an associated counterfactual. In various embodiments, thesignificance of the contrast between the one or more counterfactuals andthe subject data point may be determined according to whether aparticular threshold is exceeded, or certain features are outside aparticular range, or a combination thereof.

Certain embodiments of the invention reflect an appreciation that thedetermination of what constitutes a significant contrast may besubjective. As an example, a subject data point and an associatedcounterfactual may share the common feature of “color.” To continue theexample, the value of the “color” feature for the subject data point maybe “violet,” while the corresponding value of the “color” feature forthe counterfactual may be “lilac.” In this example, the respectivevalues of “violet” and “lilac” for the shared “color” feature may beconsidered to be of significant contrast.

In a variation of the preceding example, the value of the “color”feature for the subject data point may be “lavender,” while itscorresponding value for the counterfactual may be “lilac.” In thisvariation of the example, the respective values of “lavender” and“lilac” for the “color” feature of the subject data point andcounterfactual may or may not be considered to be of significantcontrast. Accordingly, in certain embodiments, the method by which therespective value of a particular feature shared by a subject data pointand an associated counterfactual are considered to be of significantcontrast is a matter of design choice.

In certain embodiments, the AIS impartiality assessment 1342 engine maybe configured to obtain bias ranges of features; compare the identifiedsignificant contrasts to obtained bias ranges, and determine which ofthe identified significant contrasts fall outside the obtained biasranges. In certain embodiments, the significance of the contrast may bebased upon the ranking of greatest absolute or relative differences. Incertain embodiments, the AIS impartiality assessment 1342 engine may beimplemented to present identified significant contrasts as anexplanation of the classification of the subject data point.

In certain embodiments, the AIS impartiality assessment 1342 engine maybe implemented to perform an AIS impartiality assessment operation. Incertain embodiments, performance of the AIS impartiality assessmentoperation may include assessing the impartiality of a particular MLmodel, such as the opaque model 1332. In certain embodiments, the AISimpartiality assessment of a particular opaque model 1332 may beprovided for decisions 1334 it produces for an individual user 1306, agroup of users 1306, or other entity.

In certain embodiments, performance of the AIS impartiality assessmentoperation may include the identification of significant contrastsassociated with a particular opaque model 1332. In certain embodiments,the AIS impartiality assessment 1342 engine may be implemented to use aparticular CERScore in combination with a corresponding fitness measureresulting from the use of equation (4) to perform the AIS impartialityassessment operation. In certain embodiments, the impartialityassessment operation may be performed to provide an AIS assuranceexplanation to a user 1306. In certain embodiments, the AIS assuranceexplanation may be provided to the user 1306 in the form of a cognitiveinsight 1352.

In certain embodiments, the cognitive insight 1352 provided to a usermay be in the form of an electronic message, on-screen display, printedpage, or the like. In certain embodiments, the output module 1350 may beimplemented to provide the cognitive insight 1352 to a particular user1306. In certain embodiments, the AIS assurance explanation may bepresented to the developer of the model, such that the developer canmodify the model, such as the opaque model 1332, the training corpus1302, the model trainer 1304, or a combination thereof. Those of skillin the art will recognize that the described presentation of the AISassurance explanation as an cognitive insight 1352 provides thedeveloper of the opaque model 1332 a basis for modifying the model, thetraining corpus 1302, the model trainer 1304, or a combination thereof,to achieve more robust results.

Certain embodiments of the invention reflect an appreciation that, for aparticular individual instance, the customized genetic algorithm may beimplemented to generate different counterfactuals with different valuesof a protected feature (e.g., race, age). Certain embodiments of theinvention likewise reflect an appreciation that a user 1306 can achievea desired outcome, such as decisions 1334, more easily than when thosefeatures could not be changed. Accordingly, certain embodiments of theinvention reflect an appreciation that the ability of a user 1306 tobetter understand how to achieve a desired outcome lessens thepossibility of the user 1306 claiming the opaque model 1332 was biasedagainst them.

In certain embodiments, the counterfactual engine 1336 may be configuredto determine whether a valid counterfactual can be generated, or aparticular user 1306 can achieve a better score, or a combinationthereof, by changing one or more protected features. In certainembodiments, the AIS impartiality 1342 assessment engine may beconfigured to compare scores for different groups of users 1306according to their association with a protected group (e.g., male,female, etc.) to determine whether it is more difficult for individualsin one group to change a particular decision 1334 than individuals inanother group. In certain embodiments, the counterfactual engine 1336may be implemented for use by a developer of an opaque model 1332 toaudit the AIS impartiality of the opaque model 1332 for various groupsof observations. In certain embodiments, the counterfactual engine 1336may be implemented in combination with the AIS impartiality assessment1342 engine to perform the audit.

In these embodiments, a fitness measure that is markedly different forcounterfactuals generated for different partitions of a feature's domainvalue may indicate the opaque model 1332 is biased towards one of thepartitions. For example, if the gender feature is partitioned into twovalues, male and female, and the average fitness values of generatedcounterfactuals are lower for females than for males, this could be usedas evidence that the opaque model 1332 may be biased against females.

In certain embodiments, counterfactuals and the distance function may beused in combination to calculate the overall burden for a group, asfollows:

$\begin{matrix}{{{Burden}(g)} = {\,_{g}^{\mathbb{E}}\left\lbrack {d\left( {x,c^{*}} \right)} \right\rbrack}} & (10)\end{matrix}$

where g is a partition defined by the distinct values for a specifiedfeature set.

Accordingly, Burden is related to CERScore, as it is the expected valueover a group. Certain embodiments of the invention reflect anappreciation that certain known impartiality auditing models focus onsingle features. However, Burden, as implemented in various embodiments,does not have that limitation and can be applied to any combination offeatures

In certain embodiments, Burden may be implemented to evaluate theimpartiality of a particular opaque model 1332 for a particular group ofindividuals. As an example, individuals in the training corpus 1302 usedto train the opaque model 1332 may have an associated feature of “race.”In this example, the opaque model 1332 may generate an unfavorabledecision 1334 for a certain group of the individuals who happen to be aparticular race, which can be referenced as a Burden value (i.e., aBurden score) for the group. To continue the example, the Burden valuefor the group may be higher than the Burden value for groups of otherraces that likewise receive an unfavorable decision 1334.

Accordingly, a higher Burden value may be used in certain embodiments asmeasure of bias inherent in the opaque model 1332 used to generate theunfavorable decision 1334. Accordingly, the opaque model 1332 imposes agreater burden on the group with a higher Burden value. In certainembodiments, a higher Burden value may indicate that an associated user1306 may have to make more changes to have the opaque model 1332 make amore favorable decision 1332 than a user who has a lower Burden value.Those of skill in the art will recognize that there are many ways inwhich the Burden value described herein may be used to determine thepresence of bias within a particular opaque model 1332. Accordingly, theforegoing is not intended to limit the spirit, scope, or intent of theinvention.

In certain embodiments, the AIS impartiality assessment 1342 engine maybe implemented to present identified significant contrasts that falloutside the obtained bias ranges of features as a cognitive insight1352. In certain embodiments, the cognitive insight 1352 may be in theform of an electronic message, an on-screen display, a printed page, orthe like. In certain embodiments, the output module 1350 may beimplemented to provide the cognitive insight 1352 to a particular user1306. In certain embodiments, the identified significant contrasts maybe presented as a cognitive insight 1352 to the developer of the model,such that the developer can modify the opaque model 1332, the trainingcorpus 1302, the model trainer 1304, or a combination thereof, toachieve less biased results. Skilled practitioners of the art willrecognize that the described presentation of identified significantcontrasts as a cognitive insight 1352 provides the developer of theopaque model 1332 a basis for modifying their model, the training corpus1302, the model trainer 1304, or a combination thereof.

FIG. 14 shows a subject patient chart provided as an input data pointused to generate counterfactuals implemented in accordance with anembodiment of the invention. In certain embodiments, a counterfactualengine, described in greater detail herein, may be implemented to use agenetic algorithm, likewise described in greater detail herein, togenerate a counterfactual. As likewise described in greater detailherein, the resulting counterfactual may be used in certain embodimentsto provide explainability for decisions generated by a machine learning(ML) model. In certain embodiments, a decision provided by a machinelearning model may be in the form of prediction.

In certain embodiments, the ML model may be implemented as an opaque MLmodel, described in greater detail herein. In certain embodiments, theopaque ML model may be implemented as an opaque cognitive model,likewise described in greater detail herein. In certain embodiments,counterfactuals may be implemented to a user to understand whichfeatures have the most bearing on a particular ML model's decisionbehavior.

As an example, a patient profile 1402, which can serve as an input datapoint, is shown in FIG. 14 . In this example, the patient profile 1402may include a patient identifier (ID) 1404, a predicted diabeticdiagnosis 1406, various patient attributes 1408, and associated resultsgenerated by an ML model. To continue the example, certain patientattributes 1408 may include non-modifiable 1428 and modifiable 1430diabetes factors. Examples of non-modifiable 1428 diabetes factors mayinclude the patient's age 1415, the number of pregnancies they may havehad 1414, and the thickness of their skin 1416. Likewise, examples ofmodifiable 1430 diabetes factors may include the patient's glucose level1418, their blood pressure 1420, their insulin level 1422, and body massindex (BMI) 1424.

In various embodiments, an ML model, such as an opaque cognitive model,may be implemented to process the patient attributes 1408, and theirassociated results generated by the ML model, to arrive at the predicteddiabetic diagnosis 1406. For example, as shown in the patient profile1402, the predicted diabetic diagnosis 1406 for the patient is positive.In certain embodiments, the patient attributes 1408 may be processed bya counterfactual engine, as described in greater detail herein, togenerate a different set of results 1434, which may contain one or morecounterfactuals, which in turn may result in the predicted diabeticdiagnosis 1436 for the patient being negative.

For example, as shown in the counterfactual patient profile 1432,lowering the subject patient's 1404 glucose level 1418 may be the mostoptimal 1440 counterfactual, while lowering their BMI 1424 may be a lessoptimal 1438 counterfactual. In this example, the most optimal 1440 andless optimal 1442 counterfactuals represent the least amount of changesto the subject patient's profile 1402 that will lead to a more preferredoutcome of the predicted diabetic diagnosis 1436 being negative.Accordingly, lowering the subject patient's 1404 glucose level 1418 maybe interpreted as the modifiable diabetes factor 1430 most important tochange.

In certain embodiments, a counterfactual engine may be implemented tokeep certain features constant, such as the non-modifiable 1430 diabetesfactors, while investigating features that have the ability to change,such as the modifiable 1432 diabetes factors shown in FIG. 14 .Accordingly, certain embodiments of the invention reflect anappreciation that while the modification of various features, such asthe modifiable 1432 diabetes factors, may result in a usefulcounterfactual, while other features, such as the non-modifiable 1430diabetes factors, may likewise result in useful counterfactuals, albeitnot as preferred. Furthermore, certain embodiments of the inventionreflect an appreciation that a symbiotic relationship may exist betweencertain features, whether modifiable or not, which could affect theefficacy of a particular counterfactual.

FIGS. 15 a through 15 f show a simplified depiction of the generation ofcounterfactuals implemented in accordance with an embodiment of theinvention. As shown in FIG. 15 a , an input data point 1504 and aplurality of associated patient data points 1506 are respectively mappedto an opaque cognitive model feature space 1502 according to the valuesof their associated features. As used herein, a feature space broadlyrefers to an n-dimensional collection of features used to describe dataused by a machine learning (ML) model to generate a decision, asdescribed in greater detail herein. Accordingly, as likewise usedherein, an opaque cognitive model feature space 1502 broadly refers to afeature space used by an opaque cognitive model to generate a decision,as likewise described in greater detail herein.

In this depiction, the data input point 1504 represents a subjectpatient whose associated features have been used by an opaque cognitivemodel to generate a predicted diagnosis of being diabetic. Examples ofsuch features may include the subject patient's age, gender, body massindex (BMI), glucose level, and other factors commonly used to predictdiabetes in a patient. As likewise shown in FIG. 15 a , associatedpatient 1506 data points represent individual patients whose associatedfeatures can likewise be used by the opaque cognitive model to generatea corresponding predicted diagnosis of whether or not they havediabetes.

Referring now to FIG. 15 b , the predicted diagnosis of individualpatients may be achieved by the performance of a decision generationoperation by an opaque cognitive model that classifies each associatedpatient data point 1506 shown in FIG. 15 a as either a diabetic 1508 ornon-diabetic 1510 data point. Certain embodiments of the inventionreflect an appreciation that classification models make predictionsbased upon some calculated boundary that separates different possibledecisions, such as classifications, within in its associated featurespace. Certain embodiments of the invention likewise reflect anappreciation that decision boundaries within a feature space, such asthe opaque cognitive model feature space 1502, are typically unknown inadvance and are often difficult to discover. In certain embodiments, acounterfactual engine, described in greater detail herein, may beimplemented to facilitate the determination of decision boundarieswithin a feature space by generating and classifying new data pointsthat are proximate to existing classified data points. In certainembodiments, the newly generated and classified points may be used incombination with existing classified data points to provide moregranular definition of a decision boundary.

For example, as shown in FIG. 15 c , a counterfactual engine may use anopaque cognitive model to generate new diabetic 1512 and non-diabetic1514 data points, which a that are respectively plotted to be proximateto previously classified diabetic 1508 and non-diabetic 1510 datapoints. In turn, the plotted data points 1512, 1514 can then be used incombination with the previously classified data points 1508, 1510 by thecounterfactual engine to plot a model decision boundary 1520. Aslikewise shown in FIG. 15 c , the resulting model decision boundary 1520separates two outcomes, which are whether a patient has a predicteddiagnosis of being diabetic 1522 or non-diabetic 1524. Certainembodiments of the invention reflect an appreciation that while theresulting model decision boundary 1520 may be shown as a two-dimensionalrepresentation in FIG. 15 c for visualization purposes, the modeldecision boundary 1520 actually exists in a high-dimensionalmathematical space.

In certain embodiments, a counterfactual engine may be implemented touse a genetic algorithm, as described in greater detail herein, toiteratively generate counterfactuals. In certain embodiments, thegenetic algorithm may use existing classified data points, such as theexisting classified non-diabetic 1510 data points shown in FIGS. 15 cand 15 d to generate a first generation of counterfactuals, such as thefirst generation 1516 counterfactuals likewise shown in FIG. 15 d.

In certain embodiments, the resulting first generation 1516counterfactuals may in turn be used, as depicted in FIG. 15 e , by thegenetic algorithm to generate second generation 1518 counterfactuals. Incertain embodiments, additional generations of counterfactuals may beiteratively generated by the counterfactual engine over time as neededor desired. In these embodiments, the number of counterfactualgenerations, and the interval of time over which they may be generated,is a matter of design choice. In certain embodiments, the geneticalgorithm may be implemented to generate each generation ofcounterfactuals through mutations and crossovers, as described ingreater detail herein, ultimately producing new, diverse data points.

In certain embodiments, the counterfactual engine may be implemented tosearch for the minimum feasible changes that can be made such that anopaque cognitive model predicts a different and more preferred outcome.In certain embodiments, such minimum feasible changes are associatedwith counterfactuals that are proximate to the model decision boundary1520, as shown in FIG. 15 e . In certain embodiments, the most optimalcounterfactuals may be selected according to their respective distancevector d from the input data point 1502. As used herein, an optimalcounterfactual broadly refers to a counterfactual separated from theinput data point 1502 by a distance vector d that does not exceed aparticular distance value. In these embodiments, the determination ofthe number of optimal counterfactuals, and the maximum distance value ofthe distance vector d used to define them, is a matter of design choice.

For example, as shown in FIG. 15 f , counterfactuals ‘1’ 1530, ‘2’ 1532,‘3’ 1534, and ‘4’ 1536 have been determined by a counterfactual engineto be closest to the model decision boundary 1520 of the opaquecognitive model feature space 150. Furthermore, their associateddistance vectors d₁ 1540, d₂ 1542, d₃ 1544, and d₄ 1546 do not exceed aparticular distance value. Accordingly, counterfactuals ‘1’ 1530, ‘2’1532, ‘3’ 1534, and ‘4’ 1536 are considered optimal. Furthermore,counterfactual ‘2’ 1352, has the shortest distance vector d₂ separatingit from the input data point 1502, is considered to be the most optimal.Certain embodiments of the invention reflect an appreciation that whilecounterfactual ‘2’ 1352 may be considered to be the most optimal,counterfactuals ‘1’ 1530, ‘3’ 1534, and ‘4’ 1536 may likewise providemeaningful paths to a preferable outcome.

FIG. 16 is a generalized flowchart showing the performance of AISgovernance and control operations implemented in accordance with anembodiment of the invention. In this embodiment, AIS assuranceoperations are begun in step 1602, followed by the receipt of an inputdata point, described in greater detail herein, in step 1604. An opaquecognitive model is then used in step 1606 to perform a classificationoperation to classify the input data point. The resulting classifiedinput data point is then plotted in step 1608 within an opaque cognitivemodel feature space associated with the opaque cognitive model used toperform the classification operation.

The opaque cognitive model is then used in step 1610 to plot data pointsthat are associated with the input data point within the opaquecognitive model feature space, as described in greater detail herein.Thereafter, the opaque cognitive model is likewise used to classify theplotted data points in step 1612. Once all data points have beenclassified and plotted, a counterfactual engine is used in step 1614, aslikewise described in greater detail herein, to generate and classifyadditional data points that are proximate to the previously classifieddata points.

As likewise described in greater detail herein, the counterfactualengine is then used in step 1616 to generate a model decision boundaryin the opaque cognitive model feature space. Thereafter, thecounterfactual engine uses a genetic algorithm, as described in greaterdetail herein, to generate counterfactuals in step 1618, followed by adetermination being made in step 1620 whether to generate an additionalgeneration of counterfactuals. If so, then the previously-generatedcounterfactuals are used by the genetic algorithm in step 1622 togenerate a new generation of counterfactuals, which are in turn plottedwithin the opaque cognitive model feature space in step 1624. Theprocess is then continued, proceeding with step 1620.

However, if it was determined in step 1620 to not generate an additionalgeneration of counterfactuals, then the most optimal counterfactuals areidentified, as described in greater detail herein, by their respectivedistance from the input data point within the opaque cognitive modelfeature space. As likewise described in greater detail herein, theoptimal counterfactuals are then used in step 1628 to generateexplainability of their associated decisions, as well as in step 1630 togenerate recourses as appropriate. In turn, the counterfactuals are usedin step 1632 to assess the impartiality of the opaque cognitive model,followed by using the impartiality assessment in step 1634 to generatean AIS score, as likewise described in greater detail herein. Likewise,as described in greater detail herein, the counterfactuals are then usedin step 1636 to assess the robustness of the opaque cognitive model,followed by using the robustness assessment in step 1638 to generate anAIS robustness score.

The resulting decision explainability, with explainability recourse(s),if appropriate, along with AIS bias and robustness scores, are thenprovided as a cognitive insight in step 1640. A determination is thenmade in step 1642 whether to continue AIS assurance operations. If so,then they are continued, proceeding with step 1604. Otherwise, AISassurance operations are ended in step 1644.

Although the present invention has been described in detail, it shouldbe understood that various changes, substitutions and alterations can bemade hereto without departing from the spirit and scope of the inventionas defined by the appended claims.

What is claimed is:
 1. A computer-implementable method for cognitiveinformation processing, comprising: receiving data from a plurality ofdata sources; processing the data from the plurality of data sources toprovide cognitively processed insights via an augmented intelligencesystem, the augmented intelligence system executing on a hardwareprocessor of an information processing system, the augmentedintelligence system and the information processing system providing acognitive computing function, the cognitive computing functiongenerating an associated cognitive insight; performing an explainabilitywith recourse operation, the explainability with recourse operationproviding an assurance explanation regarding the cognitive computingfunction and a recourse to changing a particular decision of thecognitive computing function, the explainability with recourse operationcomprising an explainability operation, the explainability operationproviding a rationale for the associated cognitive insight generated viathe cognitive computing function, the rationale for the associatedcognitive insight comprising an explanation of a basis used to generatethe associated cognitive insight; and, providing the cognitivelyprocessed insights to a destination, the destination comprising acognitive application, the cognitive application enabling a user tointeract with the cognitive insights; and wherein the explainabilitywith recourse operation identifies a subject data point and an optimalcounterfactual for a corresponding cognitive computing function, theoptimal counterfactual being selected from a plurality ofcounterfactuals, the optical counterfactual comprising a counterfactualdata point separated from the subject data point by a predefineddistance vector.
 2. The method of claim 1, wherein: the explainabilitywith recourse operation is performed via an explainability engine, theexplainability engine being implemented within the augmentedintelligence system, the augmented intelligence system comprising acognitive process foundation.
 3. The method of claim 2, wherein: thecognitive process foundation performs an augmented intelligenceassurance operation.
 4. The method of claim 1, wherein: theexplainability with recourse operation uses the optimal counterfactualto provide the recourse for changing the particular decision of thecognitive computing function.
 5. The method of claim 4, wherein: therecourse for the cognitive computing function is provided as a cognitiveinsight.
 6. A system comprising: a hardware processor; a data buscoupled to the hardware processor; and a non-transitory,computer-readable storage medium embodying computer program code, thenon-transitory, computer-readable storage medium being coupled to thedata bus, the computer program code interacting with a plurality ofcomputer operations and comprising instructions executable by thehardware processor and configured for: receiving data from a pluralityof data sources; processing the data from the plurality of data sourcesto provide cognitively processed insights via an augmented intelligencesystem, the augmented intelligence system executing on a hardwareprocessor of an information processing system, the augmentedintelligence system and the information processing system providing acognitive computing function, the cognitive computing functiongenerating an associated cognitive insight; performing an explainabilitywith recourse operation, the explainability with recourse operationproviding an assurance explanation regarding the cognitive computingfunction and a recourse to changing a particular decision of thecognitive computing function, the explainability with recourse operationcomprising an explainability operation, the explainability operationproviding a rationale for the associated cognitive insight generated viathe cognitive computing function, the rationale for the associatedcognitive insight comprising an explanation of a basis used to generatethe associated cognitive insight; and, providing the cognitivelyprocessed insights to a destination, the destination comprising acognitive application, the cognitive application enabling a user tointeract with the cognitive insights; and wherein the explainabilitywith recourse operation identifies a subject data point and an optimalcounterfactual for a corresponding cognitive computing function, theoptimal counterfactual being selected from a plurality ofcounterfactuals, the optical counterfactual comprising a counterfactualdata point separated from the subject data point by a predefineddistance vector.
 7. The system of claim 6, wherein: the explainabilitywith recourse operation is performed via an explainability engine, theexplainability engine being implemented within the augmentedintelligence system, the augmented intelligence system comprising acognitive process foundation.
 8. The system of claim 7, wherein: thecognitive process foundation performs an augmented intelligenceassurance operation.
 9. The system of claim 6, wherein: theexplainability with recourse operation uses the optimal counterfactualto provide recourse for changing the particular decision of thecognitive computing function.
 10. The system of claim 9, wherein: therecourse for the cognitive computing function is provided as a cognitiveinsight.
 11. A non-transitory, computer-readable storage mediumembodying computer program code, the computer program code comprisingcomputer executable instructions configured for: receiving data from aplurality of data sources; processing the data from the plurality ofdata sources to provide cognitively processed insights via an augmentedintelligence system, the augmented intelligence system executing on ahardware processor of an information processing system, the augmentedintelligence system and the information processing system providing acognitive computing function, the cognitive computing functiongenerating an associated cognitive insight; performing an explainabilitywith recourse operation, the explainability with recourse operationproviding an assurance explanation regarding the cognitive computingfunction and a recourse to changing a particular decision of thecognitive computing function, the explainability with recourse operationcomprising an explainability operation, the explainability operationproviding a rationale for the associated cognitive insight generated viathe cognitive computing function, the rationale for the associatedcognitive insight comprising an explanation of a basis used to generatethe associated cognitive insight; and, providing the cognitivelyprocessed insights to a destination, the destination comprising acognitive application, the cognitive application enabling a user tointeract with the cognitive insights; and wherein the explainabilitywith recourse operation identifies a subject data point and an optimalcounterfactual for a corresponding cognitive computing function, theoptimal counterfactual being selected from a plurality ofcounterfactuals, the optical counterfactual comprising a counterfactualdata point separated from the subject data point by a predefineddistance vector.
 12. The non-transitory, computer-readable storagemedium of claim 11, wherein: the explainability with recourse operationis performed via an explainability engine, the explainability enginebeing implemented within the augmented intelligence system, theaugmented intelligence system comprising a cognitive process foundation.13. The non-transitory, computer-readable storage medium of claim 12,wherein: the cognitive process foundation performs an augmentedintelligence assurance operation.
 14. The non-transitory,computer-readable storage medium of claim 11, wherein: theexplainability with recourse operation uses the optimal counterfactualto provide recourse for changing the particular decision of thecognitive computing function.
 15. The non-transitory, computer-readablestorage medium of claim 11, wherein: the recourse for the cognitivecomputing function is provided as a cognitive insight.
 16. Thenon-transitory, computer-readable storage medium of claim 11, wherein:the computer executable instructions are deployable to a client systemfrom a server system at a remote location.
 17. The non-transitory,computer-readable storage medium of claim 11, wherein: the computerexecutable instructions are provided by a service provider to a user onan on-demand basis.